Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.
An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.
The ratio of two odds. The exposure-odds ratio for case control data is the ratio of the odds in favor of exposure among cases to the odds in favor of exposure among noncases. The disease-odds ratio for a cohort or cross section is the ratio of the odds in favor of disease among the exposed to the odds in favor of disease among the unexposed. The prevalence-odds ratio refers to an odds ratio derived cross-sectionally from studies of prevalent cases.
A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.
Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.
Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.
The application of STATISTICS to biological systems and organisms involving the retrieval or collection, analysis, reduction, and interpretation of qualitative and quantitative data.
The total number of cases of a given disease in a specified population at a designated time. It is differentiated from INCIDENCE, which refers to the number of new cases in the population at a given time.
Social and economic factors that characterize the individual or group within the social structure.
The term "United States" in a medical context often refers to the country where a patient or study participant resides, and is not a medical term per se, but relevant for epidemiological studies, healthcare policies, and understanding differences in disease prevalence, treatment patterns, and health outcomes across various geographic locations.
A graphic means for assessing the ability of a screening test to discriminate between healthy and diseased persons; may also be used in other studies, e.g., distinguishing stimuli responses as to a faint stimuli or nonstimuli.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.
Age as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or the effect of a circumstance. It is used with human or animal concepts but should be differentiated from AGING, a physiological process, and TIME FACTORS which refers only to the passage of time.
Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.
In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.
Maleness or femaleness as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or effect of a circumstance. It is used with human or animal concepts but should be differentiated from SEX CHARACTERISTICS, anatomical or physiological manifestations of sex, and from SEX DISTRIBUTION, the number of males and females in given circumstances.
The qualitative or quantitative estimation of the likelihood of adverse effects that may result from exposure to specified health hazards or from the absence of beneficial influences. (Last, Dictionary of Epidemiology, 1988)
Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.
Inhaling and exhaling the smoke of burning TOBACCO.
Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.
Elements of limited time intervals, contributing to particular results or situations.
## I'm sorry for any confusion, but "Japan" is not a medical term or concept. It is a country located in Asia, known as Nihon-koku or Nippon-koku in Japanese, and is renowned for its unique culture, advanced technology, and rich history. If you have any questions related to medical topics, I would be happy to help answer them!
A systematic collection of factual data pertaining to health and disease in a human population within a given geographic area.
Studies in which variables relating to an individual or group of individuals are assessed over a period of time.
The status during which female mammals carry their developing young (EMBRYOS or FETUSES) in utero before birth, beginning from FERTILIZATION to BIRTH.
The study of chance processes or the relative frequency characterizing a chance process.
A country in western Europe bordered by the Atlantic Ocean, the English Channel, the Mediterranean Sea, and the countries of Belgium, Germany, Italy, Spain, Switzerland, the principalities of Andorra and Monaco, and by the duchy of Luxembourg. Its capital is Paris.
Elements of residence that characterize a population. They are applicable in determining need for and utilization of health services.
Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.
A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable.
An infant during the first month after birth.
Research techniques that focus on study designs and data gathering methods in human and animal populations.
The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from PREVALENCE, which refers to all cases, new or old, in the population at a given time.
Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)
Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease.
Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
A vital statistic measuring or recording the rate of death from any cause in hospitalized populations.
I'm sorry for any confusion, but "Italy" is not a medical term or concept, it's a country located in Southern Europe. If you have any questions related to medical topics, I'd be happy to help with those!
The presence of bacteria, viruses, and fungi in food and food products. This term is not restricted to pathogenic organisms: the presence of various non-pathogenic bacteria and fungi in cheeses and wines, for example, is included in this concept.
Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.
I'm sorry for any confusion, but "Brazil" is not a medical term or concept, it is a country located in South America, known officially as the Federative Republic of Brazil. If you have any questions related to health, medicine, or science, I'd be happy to help answer those!
Educational attainment or level of education of individuals.
A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.
Individuals whose ancestral origins are in the continent of Europe.
A latent susceptibility to disease at the genetic level, which may be activated under certain conditions.
A country spanning from central Asia to the Pacific Ocean.
Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.
Evaluation undertaken to assess the results or consequences of management and procedures used in combating disease in order to determine the efficacy, effectiveness, safety, and practicability of these interventions in individual cases or series.
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
The presence of co-existing or additional diseases with reference to an initial diagnosis or with reference to the index condition that is the subject of study. Comorbidity may affect the ability of affected individuals to function and also their survival; it may be used as a prognostic indicator for length of hospital stay, cost factors, and outcome or survival.
The level of health of the individual, group, or population as subjectively assessed by the individual or by more objective measures.
A group of people with a common cultural heritage that sets them apart from others in a variety of social relationships.
The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.
Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.
Behaviors associated with the ingesting of alcoholic beverages, including social drinking.
The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.
Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
Persons living in the United States having origins in any of the black groups of Africa.
A distribution in which a variable is distributed like the sum of the squares of any given independent random variable, each of which has a normal distribution with mean of zero and variance of one. The chi-square test is a statistical test based on comparison of a test statistic to a chi-square distribution. The oldest of these tests are used to detect whether two or more population distributions differ from one another.
An indicator of body density as determined by the relationship of BODY WEIGHT to BODY HEIGHT. BMI=weight (kg)/height squared (m2). BMI correlates with body fat (ADIPOSE TISSUE). Their relationship varies with age and gender. For adults, BMI falls into these categories: below 18.5 (underweight); 18.5-24.9 (normal); 25.0-29.9 (overweight); 30.0 and above (obese). (National Center for Health Statistics, Centers for Disease Control and Prevention)
A status with BODY WEIGHT that is grossly above the acceptable or desirable weight, usually due to accumulation of excess FATS in the body. The standards may vary with age, sex, genetic or cultural background. In the BODY MASS INDEX, a BMI greater than 30.0 kg/m2 is considered obese, and a BMI greater than 40.0 kg/m2 is considered morbidly obese (MORBID OBESITY).
The exposure to potentially harmful chemical, physical, or biological agents that occurs as a result of one's occupation.
The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.
Typical way of life or manner of living characteristic of an individual or group. (From APA, Thesaurus of Psychological Index Terms, 8th ed)
Systematic gathering of data for a particular purpose from various sources, including questionnaires, interviews, observation, existing records, and electronic devices. The process is usually preliminary to statistical analysis of the data.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.
Includes the spectrum of human immunodeficiency virus infections that range from asymptomatic seropositivity, thru AIDS-related complex (ARC), to acquired immunodeficiency syndrome (AIDS).
Tumors or cancer of the human BREAST.
Persistently high systemic arterial BLOOD PRESSURE. Based on multiple readings (BLOOD PRESSURE DETERMINATION), hypertension is currently defined as when SYSTOLIC PRESSURE is consistently greater than 140 mm Hg or when DIASTOLIC PRESSURE is consistently 90 mm Hg or more.
Measurable and quantifiable biological parameters (e.g., specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, environmental exposure and its effects, disease diagnosis, metabolic processes, substance abuse, pregnancy, cell line development, epidemiologic studies, etc.
The regular and simultaneous occurrence in a single interbreeding population of two or more discontinuous genotypes. The concept includes differences in genotypes ranging in size from a single nucleotide site (POLYMORPHISM, SINGLE NUCLEOTIDE) to large nucleotide sequences visible at a chromosomal level.
Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function.
A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.
Computer-based representation of physical systems and phenomena such as chemical processes.

Analysis of the effect of conversion from open to closed surgical intensive care unit. (1/24374)

OBJECTIVE: To compare the effect on clinical outcome of changing a surgical intensive care unit from an open to a closed unit. DESIGN: The study was carried out at a surgical intensive care unit in a large tertiary care hospital, which was changed on January 1, 1996, from an open unit, where private attending physicians contributed and controlled the care of their patients, to a closed unit, where patients' medical care was provided only by the surgical critical care team (ABS or ABA board-certified intensivists). A retrospective review was undertaken over 6 consecutive months in each system, encompassing 274 patients (125 in the open-unit period, 149 in the closed-unit period). Morbidity and mortality were compared between the two periods, along with length-of-stay (LOS) and number of consults obtained. A set of independent variables was also evaluated, including age, gender, APACHE III scores, the presence of preexisting medical conditions, the use of invasive monitoring (Swan-Ganz catheters, central and arterial lines), and the use of antibiotics, low-dose dopamine (LDD) for renal protection, vasopressors, TPN, and enteral feeding. RESULTS: Mortality (14.4% vs. 6.04%, p = 0.012) and the overall complication rate (55.84% vs. 44.14%, p = 0.002) were higher in the open-unit group versus the closed-unit group, respectively. The number of consults obtained was decreased (0.6 vs. 0.4 per patient, p = 0.036), and the rate of occurrence of renal failure was higher in the open-unit group (12.8% vs. 2.67%, p = 0.001). The mean age of the patients was similar in both groups (66.48 years vs. 66.40, p = 0.96). APACHE III scores were slightly higher in the open-unit group but did not reach statistical significance (39.02 vs. 36.16, p = 0.222). There were more men in the first group (63.2% vs. 51.3%). The use of Swan-Ganz catheters or central and arterial lines were identical, as was the use of antibiotics, TPN, and enteral feedings. The use of LDD was higher in the first group, but the LOS was identical. CONCLUSIONS: Conversion of a tertiary care surgical intensive care unit from an open to closed environment reduced dopamine usage and overall complication and mortality rates. These results support the concept that, when possible, patients in surgical intensive care units should be managed by board-certified intensivists in a closed environment.  (+info)

Antiphospholipid, anti-beta 2-glycoprotein-I and anti-oxidized-low-density-lipoprotein antibodies in antiphospholipid syndrome. (2/24374)

Antiphospholipid antibodies (aPL), anti-beta 2-glycoprotein I (anti-beta 2-GPI) and anti-oxidized-low-density lipoprotein (LDL) antibodies are all implicated in the pathogenesis of antiphospholipid syndrome. To investigate whether different autoantibodies or combinations thereof produced distinct effects related to their antigenic specificities, we examined the frequencies of antiphospholipid syndrome (APS)-related features in the presence of different antibodies [aPL, beta 2-GPI, anti-oxidized low density lipoprotein (LDL)] in 125 patients with APS. Median follow-up was 72 months: 58 patients were diagnosed as primary APS and 67 as APS plus systemic lupus erythematosus (SLE). Anticardiolipin antibodies (aCL), anti-beta 2-GPI and anti-oxidized LDL antibodies were determined by ELISA; lupus anticoagulant (LA) by standard coagulometric methods. Univariate analysis showed that patients positive for anti-beta 2-GPI had a higher risk of recurrent thrombotic events (OR = 3.64, 95% CI, p = 0.01) and pregnancy loss (OR = 2.99, 95% CI, p = 0.004). Patients positive for anti-oxidized LDL antibodies had a 2.24-fold increase in the risk of arterial thrombosis (2.24, 95% CI, p = 0.03) and lower risk of thrombocytopenia (OR = 0.41 95% CI, p = 0.04). Patients positive for aCL antibodies had a higher risk of pregnancy loss (OR = 4.62 95% CI, p = 0.001). When these data were tested by multivariate logistic regression, the association between anti-beta 2-GPI and pregnancy loss and the negative association between anti-oxidized LDL antibodies and thrombocytopenia disappeared.  (+info)

Capture-recapture models including covariate effects. (3/24374)

Capture-recapture methods are used to estimate the incidence of a disease, using a multiple-source registry. Usually, log-linear methods are used to estimate population size, assuming that not all sources of notification are dependent. Where there are categorical covariates, a stratified analysis can be performed. The multinomial logit model has occasionally been used. In this paper, the authors compare log-linear and logit models with and without covariates, and use simulated data to compare estimates from different models. The crude estimate of population size is biased when the sources are not independent. Analyses adjusting for covariates produce less biased estimates. In the absence of covariates, or where all covariates are categorical, the log-linear model and the logit model are equivalent. The log-linear model cannot include continuous variables. To minimize potential bias in estimating incidence, covariates should be included in the design and analysis of multiple-source disease registries.  (+info)

Risk factors for injuries and other health problems sustained in a marathon. (4/24374)

OBJECTIVES: To identify risk factors for injuries and other health problems occurring during or immediately after participation in a marathon. METHODS: A prospective cohort study was undertaken of participants in the 1993 Auckland Citibank marathon. Demographic data, information on running experience, training and injuries, and information on other lifestyle factors were obtained from participants before the race using an interviewer-administered questionnaire. Information on injuries and other health problems sustained during or immediately after the marathon were obtained by a self administered questionnaire. Logistic regression analyses were undertaken to identify significant risk factors for health problems. RESULTS: This study, one of only a few controlled epidemiological studies that have been undertaken of running injuries, has identified a number of risk factors for injuries and other health problems sustained in a marathon. Men were at increased risk of hamstring and calf problems, whereas women were at increased risk of hip problems. Participation in a marathon for the first time, participation in other sports, illness in the two weeks before the marathon, current use of medication, and drinking alcohol once a month or more, were associated with increased self reported risks of problems. While increased training seemed to increase the risk of front thigh and hamstring problems, it may decrease the risk of knee problems. There are significant but complex relations between age and risk of injury or health problem. CONCLUSIONS: This study has identified certain high risk subjects and risk factors for injuries and other health problems sustained in a marathon. In particular, subjects who have recently been unwell or are taking medication should weigh up carefully the pros and cons of participating.  (+info)

Early mycological treatment failure in AIDS-associated cryptococcal meningitis. (5/24374)

Cryptococcal meningitis causes significant morbidity and mortality in persons with AIDS. Of 236 AIDS patients treated with amphotericin B plus flucytosine, 29 (12%) died within 2 weeks and 62 (26%) died before 10 weeks. Just 129 (55%) of 236 patients were alive with negative cerebrospinal fluid (CSF) cultures at 10 weeks. Multivariate analyses identified that titer of cryptococcal antigen in CSF, serum albumin level, and CD4 cell count, together with dose of amphotericin B, had the strongest joint association with failure to achieve negative CSF cultures by day 14. Among patients with similar CSF cryptococcal antigen titers, CD4 cell counts, and serum albumin levels, the odds of failure at week 10 for those without negative CSF cultures by day 14 was five times that for those with negative CSF cultures by day 14 (odds ratio, 5.0; 95% confidence interval, 2.2-10.9). Prognosis is dismal for patients with AIDS-related cryptococcal meningitis. Multivariate analyses identified three components that, along with initial treatment, have the strongest joint association with early outcome. Clearly, more effective initial therapy and patient management strategies that address immune function and nutritional status are needed to improve outcomes of this disease.  (+info)

The Sock Test for evaluating activity limitation in patients with musculoskeletal pain. (6/24374)

BACKGROUND AND PURPOSE: Assessment within rehabilitation often must reflect patients' perceived functional problems and provide information on whether these problems are caused by impairments of the musculoskeletal system. Such capabilities were examined in a new functional test, the Sock Test, simulating the activity of putting on a sock. SUBJECTS AND METHODS: Intertester reliability was examined in 21 patients. Concurrent validity, responsiveness, and predictive validity were examined in a sample of 337 patients and in subgroups of this sample. RESULTS: Intertester reliability was acceptable. Sock Test scores were related to concurrent reports of activity limitation in dressing activities. Scores also reflected questionnaire-derived reports of problems in a broad range of activities of daily living and pain and were responsive to change over time. Increases in age and body mass index increased the likelihood of Sock Test scores indicating activity limitation. Pretest scores were predictive of perceived difficulties in dressing activities after 1 year. CONCLUSION AND DISCUSSION: Sock Test scores reflect perceived activity limitations and restrictions of the musculoskeletal system.  (+info)

Modified cuspal relationships of mandibular molar teeth in children with Down's syndrome. (7/24374)

A total of 50 permanent mandibular 1st molars of 26 children with Down's syndrome (DS) were examined from dental casts and 59 permanent mandibular 1st molars of normal children were examined from 33 individuals. The following measurements were performed on both right and left molars (teeth 46 and 36 respectively): (a) the intercusp distances (mb-db, mb-d, mb-dl, db-ml, db-d, db-dl, db-ml, d-dl, d-ml, dl-ml); (b) the db-mb-ml, mb-db-ml, mb-ml-db, d-mb-dl, mb-d-dl, mb-dl-d angles; (c) the area of the pentagon formed by connecting the cusp tips. All intercusp distances were significantly smaller in the DS group. Stepwise logistic regression, applied to all the intercusp distances, was used to design a multivariate probability model for DS and normals. A model based on 2 distances only, mb-dl and mb-db, proved sufficient to discriminate between the teeth of DS and the normal population. The model for tooth 36 for example was as follows: p(DS) = (e(30.6-5.6(mb-dl)+25(mb-db)))/(1 + e(30.6 5.6(mb-dl)+25(mb db))). A similar model for tooth 46 was also created, as well as a model which incorporated both teeth. With respect to the angles, significant differences between DS and normals were found in 3 out of the 6 angles which were measured: the d-mb-dl angle was smaller than in normals, the mb-d-dl angle was higher, and the mb-dl-d angle was smaller. The dl cusp was located closer to the centre of the tooth. The change in size occurs at an early stage, while the change in shape occurs in a later stage of tooth formation in the DS population.  (+info)

Organizational and environmental factors associated with nursing home participation in managed care. (8/24374)

OBJECTIVE: To develop and test a model, based on resource dependence theory, that identifies the organizational and environmental characteristics associated with nursing home participation in managed care. DATA SOURCES AND STUDY SETTING: Data for statistical analysis derived from a survey of Directors of Nursing in a sample of nursing homes in eight states (n = 308). These data were merged with data from the On-line Survey Certification and Reporting System, the Medicare Managed Care State/County Data File, and the 1995 Area Resource File. STUDY DESIGN: Since the dependent variable is dichotomous, the logistic procedure was used to fit the regression. The analysis was weighted using SUDAAN. FINDINGS: Participation in a provider network, higher proportions of resident care covered by Medicare, providing IV therapy, greater availability of RNs and physical therapists, and Medicare HMO market penetration are associated with a greater likelihood of having a managed care contract. CONCLUSION: As more Medicare recipients enroll in HMOs, nursing home involvement in managed care is likely to increase. Interorganizational linkages enhance the likelihood of managed care participation. Nursing homes interested in managed care should consider upgrading staffing and providing at least some subacute services.  (+info)

Logistic models, specifically logistic regression models, are a type of statistical analysis used in medical and epidemiological research to identify the relationship between the risk of a certain health outcome or disease (dependent variable) and one or more independent variables, such as demographic factors, exposure variables, or other clinical measurements.

In contrast to linear regression models, logistic regression models are used when the dependent variable is binary or dichotomous in nature, meaning it can only take on two values, such as "disease present" or "disease absent." The model uses a logistic function to estimate the probability of the outcome based on the independent variables.

Logistic regression models are useful for identifying risk factors and estimating the strength of associations between exposures and health outcomes, adjusting for potential confounders, and predicting the probability of an outcome given certain values of the independent variables. They can also be used to develop clinical prediction rules or scores that can aid in decision-making and patient care.

Medical Definition:

"Risk factors" are any attribute, characteristic or exposure of an individual that increases the likelihood of developing a disease or injury. They can be divided into modifiable and non-modifiable risk factors. Modifiable risk factors are those that can be changed through lifestyle choices or medical treatment, while non-modifiable risk factors are inherent traits such as age, gender, or genetic predisposition. Examples of modifiable risk factors include smoking, alcohol consumption, physical inactivity, and unhealthy diet, while non-modifiable risk factors include age, sex, and family history. It is important to note that having a risk factor does not guarantee that a person will develop the disease, but rather indicates an increased susceptibility.

The odds ratio (OR) is a statistical measure used in epidemiology and research to estimate the association between an exposure and an outcome. It represents the odds that an event will occur in one group versus the odds that it will occur in another group, assuming that all other factors are held constant.

In medical research, the odds ratio is often used to quantify the strength of the relationship between a risk factor (exposure) and a disease outcome. An OR of 1 indicates no association between the exposure and the outcome, while an OR greater than 1 suggests that there is a positive association between the two. Conversely, an OR less than 1 implies a negative association.

It's important to note that the odds ratio is not the same as the relative risk (RR), which compares the incidence rates of an outcome in two groups. While the OR can approximate the RR when the outcome is rare, they are not interchangeable and can lead to different conclusions about the association between an exposure and an outcome.

Multivariate analysis is a statistical method used to examine the relationship between multiple independent variables and a dependent variable. It allows for the simultaneous examination of the effects of two or more independent variables on an outcome, while controlling for the effects of other variables in the model. This technique can be used to identify patterns, associations, and interactions among multiple variables, and is commonly used in medical research to understand complex health outcomes and disease processes. Examples of multivariate analysis methods include multiple regression, factor analysis, cluster analysis, and discriminant analysis.

A cross-sectional study is a type of observational research design that examines the relationship between variables at one point in time. It provides a snapshot or a "cross-section" of the population at a particular moment, allowing researchers to estimate the prevalence of a disease or condition and identify potential risk factors or associations.

In a cross-sectional study, data is collected from a sample of participants at a single time point, and the variables of interest are measured simultaneously. This design can be used to investigate the association between exposure and outcome, but it cannot establish causality because it does not follow changes over time.

Cross-sectional studies can be conducted using various data collection methods, such as surveys, interviews, or medical examinations. They are often used in epidemiology to estimate the prevalence of a disease or condition in a population and to identify potential risk factors that may contribute to its development. However, because cross-sectional studies only provide a snapshot of the population at one point in time, they cannot account for changes over time or determine whether exposure preceded the outcome.

Therefore, while cross-sectional studies can be useful for generating hypotheses and identifying potential associations between variables, further research using other study designs, such as cohort or case-control studies, is necessary to establish causality and confirm any findings.

A case-control study is an observational research design used to identify risk factors or causes of a disease or health outcome. In this type of study, individuals with the disease or condition (cases) are compared with similar individuals who do not have the disease or condition (controls). The exposure history or other characteristics of interest are then compared between the two groups to determine if there is an association between the exposure and the disease.

Case-control studies are often used when it is not feasible or ethical to conduct a randomized controlled trial, as they can provide valuable insights into potential causes of diseases or health outcomes in a relatively short period of time and at a lower cost than other study designs. However, because case-control studies rely on retrospective data collection, they are subject to biases such as recall bias and selection bias, which can affect the validity of the results. Therefore, it is important to carefully design and conduct case-control studies to minimize these potential sources of bias.

Biostatistics is the application of statistics to a wide range of topics in biology, public health, and medicine. It involves the design, execution, analysis, and interpretation of statistical studies in these fields. Biostatisticians use various mathematical and statistical methods to analyze data from clinical trials, epidemiological studies, and other types of research in order to make inferences about populations and test hypotheses. They may also be involved in the development of new statistical methods for specific applications in biology and medicine.

The goals of biostatistics are to help researchers design valid and ethical studies, to ensure that data are collected and analyzed in a rigorous and unbiased manner, and to interpret the results of statistical analyses in the context of the underlying biological or medical questions. Biostatisticians may work closely with researchers in many different areas, including genetics, epidemiology, clinical trials, public health, and health services research.

Some specific tasks that biostatisticians might perform include:

* Designing studies and experiments to test hypotheses or answer research questions
* Developing sampling plans and determining sample sizes
* Collecting and managing data
* Performing statistical analyses using appropriate methods
* Interpreting the results of statistical analyses and drawing conclusions
* Communicating the results of statistical analyses to researchers, clinicians, and other stakeholders

Biostatistics is an important tool for advancing our understanding of biology and medicine, and for improving public health. It plays a key role in many areas of research, including the development of new drugs and therapies, the identification of risk factors for diseases, and the evaluation of public health interventions.

Prevalence, in medical terms, refers to the total number of people in a given population who have a particular disease or condition at a specific point in time, or over a specified period. It is typically expressed as a percentage or a ratio of the number of cases to the size of the population. Prevalence differs from incidence, which measures the number of new cases that develop during a certain period.

Socioeconomic factors are a range of interconnected conditions and influences that affect the opportunities and resources a person or group has to maintain and improve their health and well-being. These factors include:

1. Economic stability: This includes employment status, job security, income level, and poverty status. Lower income and lack of employment are associated with poorer health outcomes.
2. Education: Higher levels of education are generally associated with better health outcomes. Education can affect a person's ability to access and understand health information, as well as their ability to navigate the healthcare system.
3. Social and community context: This includes factors such as social support networks, discrimination, and community safety. Strong social supports and positive community connections are associated with better health outcomes, while discrimination and lack of safety can negatively impact health.
4. Healthcare access and quality: Access to affordable, high-quality healthcare is an important socioeconomic factor that can significantly impact a person's health. Factors such as insurance status, availability of providers, and cultural competency of healthcare systems can all affect healthcare access and quality.
5. Neighborhood and built environment: The physical conditions in which people live, work, and play can also impact their health. Factors such as housing quality, transportation options, availability of healthy foods, and exposure to environmental hazards can all influence health outcomes.

Socioeconomic factors are often interrelated and can have a cumulative effect on health outcomes. For example, someone who lives in a low-income neighborhood with limited access to healthy foods and safe parks may also face challenges related to employment, education, and healthcare access that further impact their health. Addressing socioeconomic factors is an important part of promoting health equity and reducing health disparities.

I'm sorry for any confusion, but "United States" is a geopolitical entity, specifically the name of the country consisting of 50 states, and it is not a medical term or concept. Therefore, I can't provide a medical definition for it. If you have any questions related to health, medicine, or biology, I would be happy to try to help answer those!

A Receiver Operating Characteristic (ROC) curve is a graphical representation used in medical decision-making and statistical analysis to illustrate the performance of a binary classifier system, such as a diagnostic test or a machine learning algorithm. It's a plot that shows the tradeoff between the true positive rate (sensitivity) and the false positive rate (1 - specificity) for different threshold settings.

The x-axis of an ROC curve represents the false positive rate (the proportion of negative cases incorrectly classified as positive), while the y-axis represents the true positive rate (the proportion of positive cases correctly classified as positive). Each point on the curve corresponds to a specific decision threshold, with higher points indicating better performance.

The area under the ROC curve (AUC) is a commonly used summary measure that reflects the overall performance of the classifier. An AUC value of 1 indicates perfect discrimination between positive and negative cases, while an AUC value of 0.5 suggests that the classifier performs no better than chance.

ROC curves are widely used in healthcare to evaluate diagnostic tests, predictive models, and screening tools for various medical conditions, helping clinicians make informed decisions about patient care based on the balance between sensitivity and specificity.

Statistical models are mathematical representations that describe the relationship between variables in a given dataset. They are used to analyze and interpret data in order to make predictions or test hypotheses about a population. In the context of medicine, statistical models can be used for various purposes such as:

1. Disease risk prediction: By analyzing demographic, clinical, and genetic data using statistical models, researchers can identify factors that contribute to an individual's risk of developing certain diseases. This information can then be used to develop personalized prevention strategies or early detection methods.

2. Clinical trial design and analysis: Statistical models are essential tools for designing and analyzing clinical trials. They help determine sample size, allocate participants to treatment groups, and assess the effectiveness and safety of interventions.

3. Epidemiological studies: Researchers use statistical models to investigate the distribution and determinants of health-related events in populations. This includes studying patterns of disease transmission, evaluating public health interventions, and estimating the burden of diseases.

4. Health services research: Statistical models are employed to analyze healthcare utilization, costs, and outcomes. This helps inform decisions about resource allocation, policy development, and quality improvement initiatives.

5. Biostatistics and bioinformatics: In these fields, statistical models are used to analyze large-scale molecular data (e.g., genomics, proteomics) to understand biological processes and identify potential therapeutic targets.

In summary, statistical models in medicine provide a framework for understanding complex relationships between variables and making informed decisions based on data-driven insights.

Retrospective studies, also known as retrospective research or looking back studies, are a type of observational study that examines data from the past to draw conclusions about possible causal relationships between risk factors and outcomes. In these studies, researchers analyze existing records, medical charts, or previously collected data to test a hypothesis or answer a specific research question.

Retrospective studies can be useful for generating hypotheses and identifying trends, but they have limitations compared to prospective studies, which follow participants forward in time from exposure to outcome. Retrospective studies are subject to biases such as recall bias, selection bias, and information bias, which can affect the validity of the results. Therefore, retrospective studies should be interpreted with caution and used primarily to generate hypotheses for further testing in prospective studies.

"Age factors" refer to the effects, changes, or differences that age can have on various aspects of health, disease, and medical care. These factors can encompass a wide range of issues, including:

1. Physiological changes: As people age, their bodies undergo numerous physical changes that can affect how they respond to medications, illnesses, and medical procedures. For example, older adults may be more sensitive to certain drugs or have weaker immune systems, making them more susceptible to infections.
2. Chronic conditions: Age is a significant risk factor for many chronic diseases, such as heart disease, diabetes, cancer, and arthritis. As a result, age-related medical issues are common and can impact treatment decisions and outcomes.
3. Cognitive decline: Aging can also lead to cognitive changes, including memory loss and decreased decision-making abilities. These changes can affect a person's ability to understand and comply with medical instructions, leading to potential complications in their care.
4. Functional limitations: Older adults may experience physical limitations that impact their mobility, strength, and balance, increasing the risk of falls and other injuries. These limitations can also make it more challenging for them to perform daily activities, such as bathing, dressing, or cooking.
5. Social determinants: Age-related factors, such as social isolation, poverty, and lack of access to transportation, can impact a person's ability to obtain necessary medical care and affect their overall health outcomes.

Understanding age factors is critical for healthcare providers to deliver high-quality, patient-centered care that addresses the unique needs and challenges of older adults. By taking these factors into account, healthcare providers can develop personalized treatment plans that consider a person's age, physical condition, cognitive abilities, and social circumstances.

A cohort study is a type of observational study in which a group of individuals who share a common characteristic or exposure are followed up over time to determine the incidence of a specific outcome or outcomes. The cohort, or group, is defined based on the exposure status (e.g., exposed vs. unexposed) and then monitored prospectively to assess for the development of new health events or conditions.

Cohort studies can be either prospective or retrospective in design. In a prospective cohort study, participants are enrolled and followed forward in time from the beginning of the study. In contrast, in a retrospective cohort study, researchers identify a cohort that has already been assembled through medical records, insurance claims, or other sources and then look back in time to assess exposure status and health outcomes.

Cohort studies are useful for establishing causality between an exposure and an outcome because they allow researchers to observe the temporal relationship between the two. They can also provide information on the incidence of a disease or condition in different populations, which can be used to inform public health policy and interventions. However, cohort studies can be expensive and time-consuming to conduct, and they may be subject to bias if participants are not representative of the population or if there is loss to follow-up.

The Predictive Value of Tests, specifically the Positive Predictive Value (PPV) and Negative Predictive Value (NPV), are measures used in diagnostic tests to determine the probability that a positive or negative test result is correct.

Positive Predictive Value (PPV) is the proportion of patients with a positive test result who actually have the disease. It is calculated as the number of true positives divided by the total number of positive results (true positives + false positives). A higher PPV indicates that a positive test result is more likely to be a true positive, and therefore the disease is more likely to be present.

Negative Predictive Value (NPV) is the proportion of patients with a negative test result who do not have the disease. It is calculated as the number of true negatives divided by the total number of negative results (true negatives + false negatives). A higher NPV indicates that a negative test result is more likely to be a true negative, and therefore the disease is less likely to be present.

The predictive value of tests depends on the prevalence of the disease in the population being tested, as well as the sensitivity and specificity of the test. A test with high sensitivity and specificity will generally have higher predictive values than a test with low sensitivity and specificity. However, even a highly sensitive and specific test can have low predictive values if the prevalence of the disease is low in the population being tested.

"Sex factors" is a term used in medicine and epidemiology to refer to the differences in disease incidence, prevalence, or response to treatment that are observed between males and females. These differences can be attributed to biological differences such as genetics, hormones, and anatomy, as well as social and cultural factors related to gender.

For example, some conditions such as autoimmune diseases, depression, and osteoporosis are more common in women, while others such as cardiovascular disease and certain types of cancer are more prevalent in men. Additionally, sex differences have been observed in the effectiveness and side effects of various medications and treatments.

It is important to consider sex factors in medical research and clinical practice to ensure that patients receive appropriate and effective care.

Risk assessment in the medical context refers to the process of identifying, evaluating, and prioritizing risks to patients, healthcare workers, or the community related to healthcare delivery. It involves determining the likelihood and potential impact of adverse events or hazards, such as infectious diseases, medication errors, or medical devices failures, and implementing measures to mitigate or manage those risks. The goal of risk assessment is to promote safe and high-quality care by identifying areas for improvement and taking action to minimize harm.

Prospective studies, also known as longitudinal studies, are a type of cohort study in which data is collected forward in time, following a group of individuals who share a common characteristic or exposure over a period of time. The researchers clearly define the study population and exposure of interest at the beginning of the study and follow up with the participants to determine the outcomes that develop over time. This type of study design allows for the investigation of causal relationships between exposures and outcomes, as well as the identification of risk factors and the estimation of disease incidence rates. Prospective studies are particularly useful in epidemiology and medical research when studying diseases with long latency periods or rare outcomes.

Smoking is not a medical condition, but it's a significant health risk behavior. Here is the definition from a public health perspective:

Smoking is the act of inhaling and exhaling the smoke of burning tobacco that is commonly consumed through cigarettes, pipes, and cigars. The smoke contains over 7,000 chemicals, including nicotine, tar, carbon monoxide, and numerous toxic and carcinogenic substances. These toxins contribute to a wide range of diseases and health conditions, such as lung cancer, heart disease, stroke, chronic obstructive pulmonary disease (COPD), and various other cancers, as well as adverse reproductive outcomes and negative impacts on the developing fetus during pregnancy. Smoking is highly addictive due to the nicotine content, which makes quitting smoking a significant challenge for many individuals.

A questionnaire in the medical context is a standardized, systematic, and structured tool used to gather information from individuals regarding their symptoms, medical history, lifestyle, or other health-related factors. It typically consists of a series of written questions that can be either self-administered or administered by an interviewer. Questionnaires are widely used in various areas of healthcare, including clinical research, epidemiological studies, patient care, and health services evaluation to collect data that can inform diagnosis, treatment planning, and population health management. They provide a consistent and organized method for obtaining information from large groups or individual patients, helping to ensure accurate and comprehensive data collection while minimizing bias and variability in the information gathered.

In the field of medicine, "time factors" refer to the duration of symptoms or time elapsed since the onset of a medical condition, which can have significant implications for diagnosis and treatment. Understanding time factors is crucial in determining the progression of a disease, evaluating the effectiveness of treatments, and making critical decisions regarding patient care.

For example, in stroke management, "time is brain," meaning that rapid intervention within a specific time frame (usually within 4.5 hours) is essential to administering tissue plasminogen activator (tPA), a clot-busting drug that can minimize brain damage and improve patient outcomes. Similarly, in trauma care, the "golden hour" concept emphasizes the importance of providing definitive care within the first 60 minutes after injury to increase survival rates and reduce morbidity.

Time factors also play a role in monitoring the progression of chronic conditions like diabetes or heart disease, where regular follow-ups and assessments help determine appropriate treatment adjustments and prevent complications. In infectious diseases, time factors are crucial for initiating antibiotic therapy and identifying potential outbreaks to control their spread.

Overall, "time factors" encompass the significance of recognizing and acting promptly in various medical scenarios to optimize patient outcomes and provide effective care.

I'm sorry for any confusion, but "Japan" is not a medical term. Japan is the name of a country, officially known as Nippon-koku or Nihon-koku in Japanese, and is located in East Asia. It is an island nation in the Pacific Ocean with a population of about 126 million people.

If you have any medical questions or terms that you would like me to define, please let me know!

Health surveys are research studies that collect data from a sample population to describe the current health status, health behaviors, and healthcare utilization of a particular group or community. These surveys may include questions about various aspects of health such as physical health, mental health, chronic conditions, lifestyle habits, access to healthcare services, and demographic information. The data collected from health surveys can be used to monitor trends in health over time, identify disparities in health outcomes, develop and evaluate public health programs and policies, and inform resource allocation decisions. Examples of national health surveys include the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS).

Longitudinal studies are a type of research design where data is collected from the same subjects repeatedly over a period of time, often years or even decades. These studies are used to establish patterns of changes and events over time, and can help researchers identify causal relationships between variables. They are particularly useful in fields such as epidemiology, psychology, and sociology, where the focus is on understanding developmental trends and the long-term effects of various factors on health and behavior.

In medical research, longitudinal studies can be used to track the progression of diseases over time, identify risk factors for certain conditions, and evaluate the effectiveness of treatments or interventions. For example, a longitudinal study might follow a group of individuals over several decades to assess their exposure to certain environmental factors and their subsequent development of chronic diseases such as cancer or heart disease. By comparing data collected at multiple time points, researchers can identify trends and correlations that may not be apparent in shorter-term studies.

Longitudinal studies have several advantages over other research designs, including their ability to establish temporal relationships between variables, track changes over time, and reduce the impact of confounding factors. However, they also have some limitations, such as the potential for attrition (loss of participants over time), which can introduce bias and affect the validity of the results. Additionally, longitudinal studies can be expensive and time-consuming to conduct, requiring significant resources and a long-term commitment from both researchers and study participants.

Pregnancy is a physiological state or condition where a fertilized egg (zygote) successfully implants and grows in the uterus of a woman, leading to the development of an embryo and finally a fetus. This process typically spans approximately 40 weeks, divided into three trimesters, and culminates in childbirth. Throughout this period, numerous hormonal and physical changes occur to support the growing offspring, including uterine enlargement, breast development, and various maternal adaptations to ensure the fetus's optimal growth and well-being.

In the context of medicine and healthcare, 'probability' does not have a specific medical definition. However, in general terms, probability is a branch of mathematics that deals with the study of numerical quantities called probabilities, which are assigned to events or sets of events. Probability is a measure of the likelihood that an event will occur. It is usually expressed as a number between 0 and 1, where 0 indicates that the event is impossible and 1 indicates that the event is certain to occur.

In medical research and statistics, probability is often used to quantify the uncertainty associated with statistical estimates or hypotheses. For example, a p-value is a probability that measures the strength of evidence against a hypothesis. A small p-value (typically less than 0.05) suggests that the observed data are unlikely under the assumption of the null hypothesis, and therefore provides evidence in favor of an alternative hypothesis.

Probability theory is also used to model complex systems and processes in medicine, such as disease transmission dynamics or the effectiveness of medical interventions. By quantifying the uncertainty associated with these models, researchers can make more informed decisions about healthcare policies and practices.

I'm sorry for any confusion, but "France" is not a medical term or concept. France is the largest country in Western Europe and the third-largest in Europe after Russia and Ukraine. It has been a major player in world affairs for centuries, with a significant cultural and artistic influence. If you have any questions about medical terms or concepts, I would be happy to help answer those for you.

I'm happy to help, but I need to clarify that "residence characteristics" is not a commonly used medical term. It is more related to the field of public health or epidemiology. However, if you are asking for the characteristics of a residence that could be relevant to medical or health research, they might include:

1. Housing type (single-family home, apartment, mobile home, etc.)
2. Age and condition of the housing unit
3. Presence of environmental hazards (lead paint, asbestos, radon, etc.)
4. Quality of heating, ventilation, and air conditioning systems
5. Access to clean water and sanitation facilities
6. Safety features (smoke detectors, carbon monoxide detectors, etc.)
7. Presence of pests (rodents, cockroaches, bed bugs, etc.)
8. Neighborhood characteristics (crime rates, access to healthy food options, walkability, etc.)

These factors can all have an impact on the health outcomes of individuals and communities, and are often studied in public health research.

Regression analysis is a statistical technique used in medicine, as well as in other fields, to examine the relationship between one or more independent variables (predictors) and a dependent variable (outcome). It allows for the estimation of the average change in the outcome variable associated with a one-unit change in an independent variable, while controlling for the effects of other independent variables. This technique is often used to identify risk factors for diseases or to evaluate the effectiveness of medical interventions. In medical research, regression analysis can be used to adjust for potential confounding variables and to quantify the relationship between exposures and health outcomes. It can also be used in predictive modeling to estimate the probability of a particular outcome based on multiple predictors.

A confidence interval (CI) is a range of values that is likely to contain the true value of a population parameter with a certain level of confidence. It is commonly used in statistical analysis to express the uncertainty associated with estimates derived from sample data.

For example, if we calculate a 95% confidence interval for the mean height of a population based on a sample of individuals, we can say that we are 95% confident that the true population mean height falls within the calculated range. The width of the confidence interval gives us an idea of how precise our estimate is - narrower intervals indicate more precise estimates, while wider intervals suggest greater uncertainty.

Confidence intervals are typically calculated using statistical formulas that take into account the sample size, standard deviation, and level of confidence desired. They can be used to compare different groups or to evaluate the effectiveness of interventions in medical research.

A newborn infant is a baby who is within the first 28 days of life. This period is also referred to as the neonatal period. Newborns require specialized care and attention due to their immature bodily systems and increased vulnerability to various health issues. They are closely monitored for signs of well-being, growth, and development during this critical time.

Epidemiologic methods are systematic approaches used to investigate and understand the distribution, determinants, and outcomes of health-related events or diseases in a population. These methods are applied to study the patterns of disease occurrence and transmission, identify risk factors and causes, and evaluate interventions for prevention and control. The core components of epidemiologic methods include:

1. Descriptive Epidemiology: This involves the systematic collection and analysis of data on the who, what, when, and where of health events to describe their distribution in a population. It includes measures such as incidence, prevalence, mortality, and morbidity rates, as well as geographic and temporal patterns.

2. Analytical Epidemiology: This involves the use of statistical methods to examine associations between potential risk factors and health outcomes. It includes observational studies (cohort, case-control, cross-sectional) and experimental studies (randomized controlled trials). The goal is to identify causal relationships and quantify the strength of associations.

3. Experimental Epidemiology: This involves the design and implementation of interventions or experiments to test hypotheses about disease prevention and control. It includes randomized controlled trials, community trials, and other experimental study designs.

4. Surveillance and Monitoring: This involves ongoing systematic collection, analysis, and interpretation of health-related data for early detection, tracking, and response to health events or diseases.

5. Ethical Considerations: Epidemiologic studies must adhere to ethical principles such as respect for autonomy, beneficence, non-maleficence, and justice. This includes obtaining informed consent, ensuring confidentiality, and minimizing harm to study participants.

Overall, epidemiologic methods provide a framework for investigating and understanding the complex interplay between host, agent, and environmental factors that contribute to the occurrence of health-related events or diseases in populations.

In epidemiology, the incidence of a disease is defined as the number of new cases of that disease within a specific population over a certain period of time. It is typically expressed as a rate, with the number of new cases in the numerator and the size of the population at risk in the denominator. Incidence provides information about the risk of developing a disease during a given time period and can be used to compare disease rates between different populations or to monitor trends in disease occurrence over time.

Sensitivity and specificity are statistical measures used to describe the performance of a diagnostic test or screening tool in identifying true positive and true negative results.

* Sensitivity refers to the proportion of people who have a particular condition (true positives) who are correctly identified by the test. It is also known as the "true positive rate" or "recall." A highly sensitive test will identify most or all of the people with the condition, but may also produce more false positives.
* Specificity refers to the proportion of people who do not have a particular condition (true negatives) who are correctly identified by the test. It is also known as the "true negative rate." A highly specific test will identify most or all of the people without the condition, but may also produce more false negatives.

In medical testing, both sensitivity and specificity are important considerations when evaluating a diagnostic test. High sensitivity is desirable for screening tests that aim to identify as many cases of a condition as possible, while high specificity is desirable for confirmatory tests that aim to rule out the condition in people who do not have it.

It's worth noting that sensitivity and specificity are often influenced by factors such as the prevalence of the condition in the population being tested, the threshold used to define a positive result, and the reliability and validity of the test itself. Therefore, it's important to consider these factors when interpreting the results of a diagnostic test.

Follow-up studies are a type of longitudinal research that involve repeated observations or measurements of the same variables over a period of time, in order to understand their long-term effects or outcomes. In medical context, follow-up studies are often used to evaluate the safety and efficacy of medical treatments, interventions, or procedures.

In a typical follow-up study, a group of individuals (called a cohort) who have received a particular treatment or intervention are identified and then followed over time through periodic assessments or data collection. The data collected may include information on clinical outcomes, adverse events, changes in symptoms or functional status, and other relevant measures.

The results of follow-up studies can provide important insights into the long-term benefits and risks of medical interventions, as well as help to identify factors that may influence treatment effectiveness or patient outcomes. However, it is important to note that follow-up studies can be subject to various biases and limitations, such as loss to follow-up, recall bias, and changes in clinical practice over time, which must be carefully considered when interpreting the results.

I'm sorry for any confusion, but "Linear Models" is actually a term from the field of statistics and machine learning, rather than medicine. A linear model is a type of statistical model that is used to analyze the relationship between two or more variables. In a linear model, the relationship between the dependent variable (the outcome or result) and the independent variable(s) (the factors being studied) is assumed to be linear, meaning that it can be described by a straight line on a graph.

The equation for a simple linear model with one independent variable (x) and one dependent variable (y) looks like this:

y = β0 + β1*x + ε

In this equation, β0 is the y-intercept or the value of y when x equals zero, β1 is the slope or the change in y for each unit increase in x, and ε is the error term or the difference between the actual values of y and the predicted values of y based on the linear model.

Linear models are widely used in medical research to study the relationship between various factors (such as exposure to a risk factor or treatment) and health outcomes (such as disease incidence or mortality). They can also be used to adjust for confounding variables, which are factors that may influence both the independent variable and the dependent variable, and thus affect the observed relationship between them.

Reproducibility of results in a medical context refers to the ability to obtain consistent and comparable findings when a particular experiment or study is repeated, either by the same researcher or by different researchers, following the same experimental protocol. It is an essential principle in scientific research that helps to ensure the validity and reliability of research findings.

In medical research, reproducibility of results is crucial for establishing the effectiveness and safety of new treatments, interventions, or diagnostic tools. It involves conducting well-designed studies with adequate sample sizes, appropriate statistical analyses, and transparent reporting of methods and findings to allow other researchers to replicate the study and confirm or refute the results.

The lack of reproducibility in medical research has become a significant concern in recent years, as several high-profile studies have failed to produce consistent findings when replicated by other researchers. This has led to increased scrutiny of research practices and a call for greater transparency, rigor, and standardization in the conduct and reporting of medical research.

Hospital mortality is a term used to describe the number or rate of deaths that occur in a hospital setting during a specific period. It is often used as a measure of the quality of healthcare provided by a hospital, as a higher hospital mortality rate may indicate poorer care or more complex cases being treated. However, it's important to note that hospital mortality rates can be influenced by many factors, including the severity of illness of the patients being treated, patient demographics, and the availability of resources and specialized care. Therefore, hospital mortality rates should be interpreted with caution and in the context of other quality metrics.

I'm sorry for any confusion, but "Italy" is not a medical term or concept. It is a country located in Southern Europe. If you have any questions related to medical terminology or health-related topics, I would be happy to try and help answer those for you.

Food microbiology is the study of the microorganisms that are present in food, including bacteria, viruses, fungi, and parasites. This field examines how these microbes interact with food, how they affect its safety and quality, and how they can be controlled during food production, processing, storage, and preparation. Food microbiology also involves the development of methods for detecting and identifying pathogenic microorganisms in food, as well as studying the mechanisms of foodborne illnesses and developing strategies to prevent them. Additionally, it includes research on the beneficial microbes found in certain fermented foods and their potential applications in improving food quality and safety.

A Severity of Illness Index is a measurement tool used in healthcare to assess the severity of a patient's condition and the risk of mortality or other adverse outcomes. These indices typically take into account various physiological and clinical variables, such as vital signs, laboratory values, and co-morbidities, to generate a score that reflects the patient's overall illness severity.

Examples of Severity of Illness Indices include the Acute Physiology and Chronic Health Evaluation (APACHE) system, the Simplified Acute Physiology Score (SAPS), and the Mortality Probability Model (MPM). These indices are often used in critical care settings to guide clinical decision-making, inform prognosis, and compare outcomes across different patient populations.

It is important to note that while these indices can provide valuable information about a patient's condition, they should not be used as the sole basis for clinical decision-making. Rather, they should be considered in conjunction with other factors, such as the patient's overall clinical presentation, treatment preferences, and goals of care.

I apologize, but "Brazil" is not a medical term or condition. It is the largest country in both South America and Latin America by land area and population. If you have any questions related to medical terminology or health concerns, please provide more information and I will do my best to help.

Educational status refers to the level or stage of education that a person has reached. It can be used to describe an individual's educational background, achievements, and qualifications. Educational status can be categorized in various ways, including by level (e.g., elementary school, high school, college, graduate school), years of schooling completed, or type of degree earned (e.g., bachelor's, master's, doctoral).

In medical settings, educational status may be used as a demographic variable to describe the characteristics of a patient population or to identify potential disparities in health outcomes based on education level. Research has shown that higher levels of education are often associated with better health outcomes, including lower rates of chronic diseases and improved mental health. Therefore, understanding a patient's educational status can help healthcare providers tailor their care and education strategies to meet the unique needs and challenges of each individual.

Prognosis is a medical term that refers to the prediction of the likely outcome or course of a disease, including the chances of recovery or recurrence, based on the patient's symptoms, medical history, physical examination, and diagnostic tests. It is an important aspect of clinical decision-making and patient communication, as it helps doctors and patients make informed decisions about treatment options, set realistic expectations, and plan for future care.

Prognosis can be expressed in various ways, such as percentages, categories (e.g., good, fair, poor), or survival rates, depending on the nature of the disease and the available evidence. However, it is important to note that prognosis is not an exact science and may vary depending on individual factors, such as age, overall health status, and response to treatment. Therefore, it should be used as a guide rather than a definitive forecast.

The term "European Continental Ancestry Group" is a medical/ethnic classification that refers to individuals who trace their genetic ancestry to the continent of Europe. This group includes people from various ethnic backgrounds and nationalities, such as Northern, Southern, Eastern, and Western European descent. It is often used in research and medical settings for population studies or to identify genetic patterns and predispositions to certain diseases that may be more common in specific ancestral groups. However, it's important to note that this classification can oversimplify the complex genetic diversity within and between populations, and should be used with caution.

Genetic predisposition to disease refers to an increased susceptibility or vulnerability to develop a particular illness or condition due to inheriting specific genetic variations or mutations from one's parents. These genetic factors can make it more likely for an individual to develop a certain disease, but it does not guarantee that the person will definitely get the disease. Environmental factors, lifestyle choices, and interactions between genes also play crucial roles in determining if a genetically predisposed person will actually develop the disease. It is essential to understand that having a genetic predisposition only implies a higher risk, not an inevitable outcome.

I am not aware of a specific medical definition for the term "China." Generally, it is used to refer to:

1. The People's Republic of China (PRC), which is a country in East Asia. It is the most populous country in the world and the fourth largest by geographical area. Its capital city is Beijing.
2. In a historical context, "China" was used to refer to various dynasties and empires that existed in East Asia over thousands of years. The term "Middle Kingdom" or "Zhongguo" (中国) has been used by the Chinese people to refer to their country for centuries.
3. In a more general sense, "China" can also be used to describe products or goods that originate from or are associated with the People's Republic of China.

If you have a specific context in which you encountered the term "China" related to medicine, please provide it so I can give a more accurate response.

"Likelihood functions" is a statistical concept that is used in medical research and other fields to estimate the probability of obtaining a given set of data, given a set of assumptions or parameters. In other words, it is a function that describes how likely it is to observe a particular outcome or result, based on a set of model parameters.

More formally, if we have a statistical model that depends on a set of parameters θ, and we observe some data x, then the likelihood function is defined as:

L(θ | x) = P(x | θ)

This means that the likelihood function describes the probability of observing the data x, given a particular value of the parameter vector θ. By convention, the likelihood function is often expressed as a function of the parameters, rather than the data, so we might instead write:

L(θ) = P(x | θ)

The likelihood function can be used to estimate the values of the model parameters that are most consistent with the observed data. This is typically done by finding the value of θ that maximizes the likelihood function, which is known as the maximum likelihood estimator (MLE). The MLE has many desirable statistical properties, including consistency, efficiency, and asymptotic normality.

In medical research, likelihood functions are often used in the context of Bayesian analysis, where they are combined with prior distributions over the model parameters to obtain posterior distributions that reflect both the observed data and prior knowledge or assumptions about the parameter values. This approach is particularly useful when there is uncertainty or ambiguity about the true value of the parameters, as it allows researchers to incorporate this uncertainty into their analyses in a principled way.

Treatment outcome is a term used to describe the result or effect of medical treatment on a patient's health status. It can be measured in various ways, such as through symptoms improvement, disease remission, reduced disability, improved quality of life, or survival rates. The treatment outcome helps healthcare providers evaluate the effectiveness of a particular treatment plan and make informed decisions about future care. It is also used in clinical research to compare the efficacy of different treatments and improve patient care.

Statistical data interpretation involves analyzing and interpreting numerical data in order to identify trends, patterns, and relationships. This process often involves the use of statistical methods and tools to organize, summarize, and draw conclusions from the data. The goal is to extract meaningful insights that can inform decision-making, hypothesis testing, or further research.

In medical contexts, statistical data interpretation is used to analyze and make sense of large sets of clinical data, such as patient outcomes, treatment effectiveness, or disease prevalence. This information can help healthcare professionals and researchers better understand the relationships between various factors that impact health outcomes, develop more effective treatments, and identify areas for further study.

Some common statistical methods used in data interpretation include descriptive statistics (e.g., mean, median, mode), inferential statistics (e.g., hypothesis testing, confidence intervals), and regression analysis (e.g., linear, logistic). These methods can help medical professionals identify patterns and trends in the data, assess the significance of their findings, and make evidence-based recommendations for patient care or public health policy.

Comorbidity is the presence of one or more additional health conditions or diseases alongside a primary illness or condition. These co-occurring health issues can have an impact on the treatment plan, prognosis, and overall healthcare management of an individual. Comorbidities often interact with each other and the primary condition, leading to more complex clinical situations and increased healthcare needs. It is essential for healthcare professionals to consider and address comorbidities to provide comprehensive care and improve patient outcomes.

Health status is a term used to describe the overall condition of an individual's health, including physical, mental, and social well-being. It is often assessed through various measures such as medical history, physical examination, laboratory tests, and self-reported health assessments. Health status can be used to identify health disparities, track changes in population health over time, and evaluate the effectiveness of healthcare interventions.

An ethnic group is a category of people who identify with each other based on shared ancestry, language, culture, history, and/or physical characteristics. The concept of an ethnic group is often used in the social sciences to describe a population that shares a common identity and a sense of belonging to a larger community.

Ethnic groups can be distinguished from racial groups, which are categories of people who are defined by their physical characteristics, such as skin color, hair texture, and facial features. While race is a social construct based on physical differences, ethnicity is a cultural construct based on shared traditions, beliefs, and practices.

It's important to note that the concept of ethnic groups can be complex and fluid, as individuals may identify with multiple ethnic groups or switch their identification over time. Additionally, the boundaries between different ethnic groups can be blurred and contested, and the ways in which people define and categorize themselves and others can vary across cultures and historical periods.

Genotype, in genetics, refers to the complete heritable genetic makeup of an individual organism, including all of its genes. It is the set of instructions contained in an organism's DNA for the development and function of that organism. The genotype is the basis for an individual's inherited traits, and it can be contrasted with an individual's phenotype, which refers to the observable physical or biochemical characteristics of an organism that result from the expression of its genes in combination with environmental influences.

It is important to note that an individual's genotype is not necessarily identical to their genetic sequence. Some genes have multiple forms called alleles, and an individual may inherit different alleles for a given gene from each parent. The combination of alleles that an individual inherits for a particular gene is known as their genotype for that gene.

Understanding an individual's genotype can provide important information about their susceptibility to certain diseases, their response to drugs and other treatments, and their risk of passing on inherited genetic disorders to their offspring.

Biological models, also known as physiological models or organismal models, are simplified representations of biological systems, processes, or mechanisms that are used to understand and explain the underlying principles and relationships. These models can be theoretical (conceptual or mathematical) or physical (such as anatomical models, cell cultures, or animal models). They are widely used in biomedical research to study various phenomena, including disease pathophysiology, drug action, and therapeutic interventions.

Examples of biological models include:

1. Mathematical models: These use mathematical equations and formulas to describe complex biological systems or processes, such as population dynamics, metabolic pathways, or gene regulation networks. They can help predict the behavior of these systems under different conditions and test hypotheses about their underlying mechanisms.
2. Cell cultures: These are collections of cells grown in a controlled environment, typically in a laboratory dish or flask. They can be used to study cellular processes, such as signal transduction, gene expression, or metabolism, and to test the effects of drugs or other treatments on these processes.
3. Animal models: These are living organisms, usually vertebrates like mice, rats, or non-human primates, that are used to study various aspects of human biology and disease. They can provide valuable insights into the pathophysiology of diseases, the mechanisms of drug action, and the safety and efficacy of new therapies.
4. Anatomical models: These are physical representations of biological structures or systems, such as plastic models of organs or tissues, that can be used for educational purposes or to plan surgical procedures. They can also serve as a basis for developing more sophisticated models, such as computer simulations or 3D-printed replicas.

Overall, biological models play a crucial role in advancing our understanding of biology and medicine, helping to identify new targets for therapeutic intervention, develop novel drugs and treatments, and improve human health.

'Alcohol drinking' refers to the consumption of alcoholic beverages, which contain ethanol (ethyl alcohol) as the active ingredient. Ethanol is a central nervous system depressant that can cause euphoria, disinhibition, and sedation when consumed in small to moderate amounts. However, excessive drinking can lead to alcohol intoxication, with symptoms ranging from slurred speech and impaired coordination to coma and death.

Alcohol is metabolized in the liver by enzymes such as alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH). The breakdown of ethanol produces acetaldehyde, a toxic compound that can cause damage to various organs in the body. Chronic alcohol drinking can lead to a range of health problems, including liver disease, pancreatitis, cardiovascular disease, neurological disorders, and increased risk of cancer.

Moderate drinking is generally defined as up to one drink per day for women and up to two drinks per day for men, where a standard drink contains about 14 grams (0.6 ounces) of pure alcohol. However, it's important to note that there are no safe levels of alcohol consumption, and any level of drinking carries some risk to health.

In the context of medicine, risk is the probability or likelihood of an adverse health effect or the occurrence of a negative event related to treatment or exposure to certain hazards. It is usually expressed as a ratio or percentage and can be influenced by various factors such as age, gender, lifestyle, genetics, and environmental conditions. Risk assessment involves identifying, quantifying, and prioritizing risks to make informed decisions about prevention, mitigation, or treatment strategies.

The term "Theoretical Models" is used in various scientific fields, including medicine, to describe a representation of a complex system or phenomenon. It is a simplified framework that explains how different components of the system interact with each other and how they contribute to the overall behavior of the system. Theoretical models are often used in medical research to understand and predict the outcomes of diseases, treatments, or public health interventions.

A theoretical model can take many forms, such as mathematical equations, computer simulations, or conceptual diagrams. It is based on a set of assumptions and hypotheses about the underlying mechanisms that drive the system. By manipulating these variables and observing the effects on the model's output, researchers can test their assumptions and generate new insights into the system's behavior.

Theoretical models are useful for medical research because they allow scientists to explore complex systems in a controlled and systematic way. They can help identify key drivers of disease or treatment outcomes, inform the design of clinical trials, and guide the development of new interventions. However, it is important to recognize that theoretical models are simplifications of reality and may not capture all the nuances and complexities of real-world systems. Therefore, they should be used in conjunction with other forms of evidence, such as experimental data and observational studies, to inform medical decision-making.

African Americans are defined as individuals who have ancestry from any of the black racial groups of Africa. This term is often used to describe people living in the United States who have total or partial descent from enslaved African peoples. The term does not refer to a single ethnicity but is a broad term that includes various ethnic groups with diverse cultures, languages, and traditions. It's important to note that some individuals may prefer to identify as Black or of African descent rather than African American, depending on their personal identity and background.

The Chi-square distribution is a continuous probability distribution that is often used in statistical hypothesis testing. It is the distribution of a sum of squares of k independent standard normal random variables. The resulting quantity follows a chi-square distribution with k degrees of freedom, denoted as χ²(k).

The probability density function (pdf) of the Chi-square distribution with k degrees of freedom is given by:

f(x; k) = (1/ (2^(k/2) * Γ(k/2))) \* x^((k/2)-1) \* e^(-x/2), for x > 0 and 0, otherwise.

Where Γ(k/2) is the gamma function evaluated at k/2. The mean and variance of a Chi-square distribution with k degrees of freedom are k and 2k, respectively.

The Chi-square distribution has various applications in statistical inference, including testing goodness-of-fit, homogeneity of variances, and independence in contingency tables.

Body Mass Index (BMI) is a measure used to assess whether a person has a healthy weight for their height. It's calculated by dividing a person's weight in kilograms by the square of their height in meters. Here is the medical definition:

Body Mass Index (BMI) = weight(kg) / [height(m)]^2

According to the World Health Organization, BMI categories are defined as follows:

* Less than 18.5: Underweight
* 18.5-24.9: Normal or healthy weight
* 25.0-29.9: Overweight
* 30.0 and above: Obese

It is important to note that while BMI can be a useful tool for identifying weight issues in populations, it does have limitations when applied to individuals. For example, it may not accurately reflect body fat distribution or muscle mass, which can affect health risks associated with excess weight. Therefore, BMI should be used as one of several factors when evaluating an individual's health status and risk for chronic diseases.

Obesity is a complex disease characterized by an excess accumulation of body fat to the extent that it negatively impacts health. It's typically defined using Body Mass Index (BMI), a measure calculated from a person's weight and height. A BMI of 30 or higher is indicative of obesity. However, it's important to note that while BMI can be a useful tool for identifying obesity in populations, it does not directly measure body fat and may not accurately reflect health status in individuals. Other factors such as waist circumference, blood pressure, cholesterol levels, and blood sugar levels should also be considered when assessing health risks associated with weight.

Occupational exposure refers to the contact of an individual with potentially harmful chemical, physical, or biological agents as a result of their job or occupation. This can include exposure to hazardous substances such as chemicals, heavy metals, or dusts; physical agents such as noise, radiation, or ergonomic stressors; and biological agents such as viruses, bacteria, or fungi.

Occupational exposure can occur through various routes, including inhalation, skin contact, ingestion, or injection. Prolonged or repeated exposure to these hazards can increase the risk of developing acute or chronic health conditions, such as respiratory diseases, skin disorders, neurological damage, or cancer.

Employers have a legal and ethical responsibility to minimize occupational exposures through the implementation of appropriate control measures, including engineering controls, administrative controls, personal protective equipment, and training programs. Regular monitoring and surveillance of workers' health can also help identify and prevent potential health hazards in the workplace.

Statistics, as a topic in the context of medicine and healthcare, refers to the scientific discipline that involves the collection, analysis, interpretation, and presentation of numerical data or quantifiable data in a meaningful and organized manner. It employs mathematical theories and models to draw conclusions, make predictions, and support evidence-based decision-making in various areas of medical research and practice.

Some key concepts and methods in medical statistics include:

1. Descriptive Statistics: Summarizing and visualizing data through measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation).
2. Inferential Statistics: Drawing conclusions about a population based on a sample using hypothesis testing, confidence intervals, and statistical modeling.
3. Probability Theory: Quantifying the likelihood of events or outcomes in medical scenarios, such as diagnostic tests' sensitivity and specificity.
4. Study Designs: Planning and implementing various research study designs, including randomized controlled trials (RCTs), cohort studies, case-control studies, and cross-sectional surveys.
5. Sampling Methods: Selecting a representative sample from a population to ensure the validity and generalizability of research findings.
6. Multivariate Analysis: Examining the relationships between multiple variables simultaneously using techniques like regression analysis, factor analysis, or cluster analysis.
7. Survival Analysis: Analyzing time-to-event data, such as survival rates in clinical trials or disease progression.
8. Meta-Analysis: Systematically synthesizing and summarizing the results of multiple studies to provide a comprehensive understanding of a research question.
9. Biostatistics: A subfield of statistics that focuses on applying statistical methods to biological data, including medical research.
10. Epidemiology: The study of disease patterns in populations, which often relies on statistical methods for data analysis and interpretation.

Medical statistics is essential for evidence-based medicine, clinical decision-making, public health policy, and healthcare management. It helps researchers and practitioners evaluate the effectiveness and safety of medical interventions, assess risk factors and outcomes associated with diseases or treatments, and monitor trends in population health.

I'm sorry for any confusion, but "life style" is not a term that has a specific medical definition. It generally refers to the way an individual or group lives, including their habits, behaviors, and preferences in areas such as diet, exercise, recreation, and stress management. Some lifestyle factors can have a significant impact on health outcomes and risk for certain diseases. However, it is not a medical term with a specific clinical meaning.

Data collection in the medical context refers to the systematic gathering of information relevant to a specific research question or clinical situation. This process involves identifying and recording data elements, such as demographic characteristics, medical history, physical examination findings, laboratory results, and imaging studies, from various sources including patient interviews, medical records, and diagnostic tests. The data collected is used to support clinical decision-making, inform research hypotheses, and evaluate the effectiveness of treatments or interventions. It is essential that data collection is performed in a standardized and unbiased manner to ensure the validity and reliability of the results.

An algorithm is not a medical term, but rather a concept from computer science and mathematics. In the context of medicine, algorithms are often used to describe step-by-step procedures for diagnosing or managing medical conditions. These procedures typically involve a series of rules or decision points that help healthcare professionals make informed decisions about patient care.

For example, an algorithm for diagnosing a particular type of heart disease might involve taking a patient's medical history, performing a physical exam, ordering certain diagnostic tests, and interpreting the results in a specific way. By following this algorithm, healthcare professionals can ensure that they are using a consistent and evidence-based approach to making a diagnosis.

Algorithms can also be used to guide treatment decisions. For instance, an algorithm for managing diabetes might involve setting target blood sugar levels, recommending certain medications or lifestyle changes based on the patient's individual needs, and monitoring the patient's response to treatment over time.

Overall, algorithms are valuable tools in medicine because they help standardize clinical decision-making and ensure that patients receive high-quality care based on the latest scientific evidence.

Single Nucleotide Polymorphism (SNP) is a type of genetic variation that occurs when a single nucleotide (A, T, C, or G) in the DNA sequence is altered. This alteration must occur in at least 1% of the population to be considered a SNP. These variations can help explain why some people are more susceptible to certain diseases than others and can also influence how an individual responds to certain medications. SNPs can serve as biological markers, helping scientists locate genes that are associated with disease. They can also provide information about an individual's ancestry and ethnic background.

HIV (Human Immunodeficiency Virus) infection is a viral illness that progressively attacks and weakens the immune system, making individuals more susceptible to other infections and diseases. The virus primarily infects CD4+ T cells, a type of white blood cell essential for fighting off infections. Over time, as the number of these immune cells declines, the body becomes increasingly vulnerable to opportunistic infections and cancers.

HIV infection has three stages:

1. Acute HIV infection: This is the initial stage that occurs within 2-4 weeks after exposure to the virus. During this period, individuals may experience flu-like symptoms such as fever, fatigue, rash, swollen glands, and muscle aches. The virus replicates rapidly, and the viral load in the body is very high.
2. Chronic HIV infection (Clinical latency): This stage follows the acute infection and can last several years if left untreated. Although individuals may not show any symptoms during this phase, the virus continues to replicate at low levels, and the immune system gradually weakens. The viral load remains relatively stable, but the number of CD4+ T cells declines over time.
3. AIDS (Acquired Immunodeficiency Syndrome): This is the most advanced stage of HIV infection, characterized by a severely damaged immune system and numerous opportunistic infections or cancers. At this stage, the CD4+ T cell count drops below 200 cells/mm3 of blood.

It's important to note that with proper antiretroviral therapy (ART), individuals with HIV infection can effectively manage the virus, maintain a healthy immune system, and significantly reduce the risk of transmission to others. Early diagnosis and treatment are crucial for improving long-term health outcomes and reducing the spread of HIV.

Breast neoplasms refer to abnormal growths in the breast tissue that can be benign or malignant. Benign breast neoplasms are non-cancerous tumors or growths, while malignant breast neoplasms are cancerous tumors that can invade surrounding tissues and spread to other parts of the body.

Breast neoplasms can arise from different types of cells in the breast, including milk ducts, milk sacs (lobules), or connective tissue. The most common type of breast cancer is ductal carcinoma, which starts in the milk ducts and can spread to other parts of the breast and nearby structures.

Breast neoplasms are usually detected through screening methods such as mammography, ultrasound, or MRI, or through self-examination or clinical examination. Treatment options for breast neoplasms depend on several factors, including the type and stage of the tumor, the patient's age and overall health, and personal preferences. Treatment may include surgery, radiation therapy, chemotherapy, hormone therapy, or targeted therapy.

Hypertension is a medical term used to describe abnormally high blood pressure in the arteries, often defined as consistently having systolic blood pressure (the top number in a blood pressure reading) over 130 mmHg and/or diastolic blood pressure (the bottom number) over 80 mmHg. It is also commonly referred to as high blood pressure.

Hypertension can be classified into two types: primary or essential hypertension, which has no identifiable cause and accounts for about 95% of cases, and secondary hypertension, which is caused by underlying medical conditions such as kidney disease, hormonal disorders, or use of certain medications.

If left untreated, hypertension can lead to serious health complications such as heart attack, stroke, heart failure, and chronic kidney disease. Therefore, it is important for individuals with hypertension to manage their condition through lifestyle modifications (such as healthy diet, regular exercise, stress management) and medication if necessary, under the guidance of a healthcare professional.

A biological marker, often referred to as a biomarker, is a measurable indicator that reflects the presence or severity of a disease state, or a response to a therapeutic intervention. Biomarkers can be found in various materials such as blood, tissues, or bodily fluids, and they can take many forms, including molecular, histologic, radiographic, or physiological measurements.

In the context of medical research and clinical practice, biomarkers are used for a variety of purposes, such as:

1. Diagnosis: Biomarkers can help diagnose a disease by indicating the presence or absence of a particular condition. For example, prostate-specific antigen (PSA) is a biomarker used to detect prostate cancer.
2. Monitoring: Biomarkers can be used to monitor the progression or regression of a disease over time. For instance, hemoglobin A1c (HbA1c) levels are monitored in diabetes patients to assess long-term blood glucose control.
3. Predicting: Biomarkers can help predict the likelihood of developing a particular disease or the risk of a negative outcome. For example, the presence of certain genetic mutations can indicate an increased risk for breast cancer.
4. Response to treatment: Biomarkers can be used to evaluate the effectiveness of a specific treatment by measuring changes in the biomarker levels before and after the intervention. This is particularly useful in personalized medicine, where treatments are tailored to individual patients based on their unique biomarker profiles.

It's important to note that for a biomarker to be considered clinically valid and useful, it must undergo rigorous validation through well-designed studies, including demonstrating sensitivity, specificity, reproducibility, and clinical relevance.

Genetic polymorphism refers to the occurrence of multiple forms (called alleles) of a particular gene within a population. These variations in the DNA sequence do not generally affect the function or survival of the organism, but they can contribute to differences in traits among individuals. Genetic polymorphisms can be caused by single nucleotide changes (SNPs), insertions or deletions of DNA segments, or other types of genetic rearrangements. They are important for understanding genetic diversity and evolution, as well as for identifying genetic factors that may contribute to disease susceptibility in humans.

Genetic models are theoretical frameworks used in genetics to describe and explain the inheritance patterns and genetic architecture of traits, diseases, or phenomena. These models are based on mathematical equations and statistical methods that incorporate information about gene frequencies, modes of inheritance, and the effects of environmental factors. They can be used to predict the probability of certain genetic outcomes, to understand the genetic basis of complex traits, and to inform medical management and treatment decisions.

There are several types of genetic models, including:

1. Mendelian models: These models describe the inheritance patterns of simple genetic traits that follow Mendel's laws of segregation and independent assortment. Examples include autosomal dominant, autosomal recessive, and X-linked inheritance.
2. Complex trait models: These models describe the inheritance patterns of complex traits that are influenced by multiple genes and environmental factors. Examples include heart disease, diabetes, and cancer.
3. Population genetics models: These models describe the distribution and frequency of genetic variants within populations over time. They can be used to study evolutionary processes, such as natural selection and genetic drift.
4. Quantitative genetics models: These models describe the relationship between genetic variation and phenotypic variation in continuous traits, such as height or IQ. They can be used to estimate heritability and to identify quantitative trait loci (QTLs) that contribute to trait variation.
5. Statistical genetics models: These models use statistical methods to analyze genetic data and infer the presence of genetic associations or linkage. They can be used to identify genetic risk factors for diseases or traits.

Overall, genetic models are essential tools in genetics research and medical genetics, as they allow researchers to make predictions about genetic outcomes, test hypotheses about the genetic basis of traits and diseases, and develop strategies for prevention, diagnosis, and treatment.

Survival analysis is a branch of statistics that deals with the analysis of time to event data. It is used to estimate the time it takes for a certain event of interest to occur, such as death, disease recurrence, or treatment failure. The event of interest is called the "failure" event, and survival analysis estimates the probability of not experiencing the failure event until a certain point in time, also known as the "survival" probability.

Survival analysis can provide important information about the effectiveness of treatments, the prognosis of patients, and the identification of risk factors associated with the event of interest. It can handle censored data, which is common in medical research where some participants may drop out or be lost to follow-up before the event of interest occurs.

Survival analysis typically involves estimating the survival function, which describes the probability of surviving beyond a certain time point, as well as hazard functions, which describe the instantaneous rate of failure at a given time point. Other important concepts in survival analysis include median survival times, restricted mean survival times, and various statistical tests to compare survival curves between groups.

Analysis of Variance (ANOVA) is a statistical technique used to compare the means of two or more groups and determine whether there are any significant differences between them. It is a way to analyze the variance in a dataset to determine whether the variability between groups is greater than the variability within groups, which can indicate that the groups are significantly different from one another.

ANOVA is based on the concept of partitioning the total variance in a dataset into two components: variance due to differences between group means (also known as "between-group variance") and variance due to differences within each group (also known as "within-group variance"). By comparing these two sources of variance, ANOVA can help researchers determine whether any observed differences between groups are statistically significant, or whether they could have occurred by chance.

ANOVA is a widely used technique in many areas of research, including biology, psychology, engineering, and business. It is often used to compare the means of two or more experimental groups, such as a treatment group and a control group, to determine whether the treatment had a significant effect. ANOVA can also be used to compare the means of different populations or subgroups within a population, to identify any differences that may exist between them.

A computer simulation is a process that involves creating a model of a real-world system or phenomenon on a computer and then using that model to run experiments and make predictions about how the system will behave under different conditions. In the medical field, computer simulations are used for a variety of purposes, including:

1. Training and education: Computer simulations can be used to create realistic virtual environments where medical students and professionals can practice their skills and learn new procedures without risk to actual patients. For example, surgeons may use simulation software to practice complex surgical techniques before performing them on real patients.
2. Research and development: Computer simulations can help medical researchers study the behavior of biological systems at a level of detail that would be difficult or impossible to achieve through experimental methods alone. By creating detailed models of cells, tissues, organs, or even entire organisms, researchers can use simulation software to explore how these systems function and how they respond to different stimuli.
3. Drug discovery and development: Computer simulations are an essential tool in modern drug discovery and development. By modeling the behavior of drugs at a molecular level, researchers can predict how they will interact with their targets in the body and identify potential side effects or toxicities. This information can help guide the design of new drugs and reduce the need for expensive and time-consuming clinical trials.
4. Personalized medicine: Computer simulations can be used to create personalized models of individual patients based on their unique genetic, physiological, and environmental characteristics. These models can then be used to predict how a patient will respond to different treatments and identify the most effective therapy for their specific condition.

Overall, computer simulations are a powerful tool in modern medicine, enabling researchers and clinicians to study complex systems and make predictions about how they will behave under a wide range of conditions. By providing insights into the behavior of biological systems at a level of detail that would be difficult or impossible to achieve through experimental methods alone, computer simulations are helping to advance our understanding of human health and disease.

... may refer to: Logistic function - a continuous sigmoidal curve Logistic map - a discrete version, which exhibits ... chaotic behavior Logistic regression This disambiguation page lists articles associated with the title Logistic model. If an ...
... s are based on the earlier idea of a model tree: a decision tree that has linear regression models at its ... a logistic model tree (LMT) is a classification model with an associated supervised training algorithm that combines logistic ... Logistic model trees (PDF). ECML PKDD. Landwehr, N.; Hall, M.; Frank, E. (2005). "Logistic Model Trees" (PDF). Machine Learning ... doi:10.1007/s10994-005-0466-3. Sumner, Marc; Eibe Frank; Mark Hall (2005). Speeding up logistic model tree induction (PDF). ...
"What is Reverse Logistics?", Reverse Logistics Magazine, Winter/Spring 2006. Fleischmann, Moritz. "Reverse logistics network ... In order to model reverse logistics network from an economics point of view, the following simplified reverse logistics system ... "Reverse logistics network design: Review of models and solution techniques". academia.edu. Retrieved 3 June 2015. (All articles ... According to the introduced model the main differences between forward and reverse logistics can be identified: Uncertainty on ...
"Fifth Party Logistic Model (5PL)". LogisticsGlossary. Retrieved 21 September 2018. Raue, Jan Simon; Wieland, Andreas (2015). " ... The logistics department of a producing firm can also be a first party logistics provider if they have own transport assets and ... Logistics is the core competence of third-party logistics providers. Providers may have better related knowledge and greater ... Second-party logistics providers (2PL) are service providers which provide their specialized logistics services in a larger ( ...
The goal of multinomial logistic regression is to construct a model that explains the relationship between the explanatory ... Logistic} (0,1)} then b X ∼ Logistic ⁡ ( 0 , b ) . {\displaystyle bX\sim \operatorname {Logistic} (0,b).} This means that the ... There are multiple equivalent ways to describe the mathematical model underlying multinomial logistic regression. This can make ... The article on logistic regression presents a number of equivalent formulations of simple logistic regression, and many of ...
The log-logistic has been used as a simple model of the distribution of wealth or income in economics, where it is known as the ... The log-logistic distribution can be used as the basis of an accelerated failure time model by allowing α {\displaystyle \alpha ... The log-logistic distribution has been used in hydrology for modelling stream flow rates and precipitation. Extreme values like ... Its Gini coefficient is 1 / β {\displaystyle 1/\beta } . The log-logistic has been used as a model for the period of time ...
Hilbe, J. M. (2009). Logistic Regression Models. Chapman & Hall/CRC Press. ISBN 978-1-4200-7575-5. Mika, S.; et al. (1999). " ... However, when discriminant analysis' assumptions are met, it is more powerful than logistic regression. Unlike logistic ... Edward Altman's 1968 model is still a leading model in practical applications. In computerised face recognition, each face is ... Logistic regression or other methods are now more commonly used. The use of discriminant analysis in marketing can be described ...
Landwehr, N.; Hall, M.; Frank, E. (2005). "Logistic Model Trees" (PDF). Machine Learning. 59 (1-2): 161-205. doi:10.1007/s10994 ... Muggeo, V. M. R. (2008). "Segmented: an R package to fit regression models with broken-line relationships" (PDF). R News. 8: 20 ... Muggeo, V. M. R. (2003). "Estimating regression models with unknown break‐points". Statistics in Medicine. 22 (19): 3055-3071. ...
The complexity of logistics can be modeled, analyzed, visualized, and optimized by dedicated simulation software. The ... logistics Distribution logistics After-sales logistics Disposal logistics Reverse logistics Green logistics Global logistics ... material logistics Emergency logistics Production logistics Construction logistics Capital project logistics Digital logistics ... RAM logistics (see also Logistic engineering) combines both business logistics and military logistics since it is concerned ...
The three-parameter logistic model relaxes both these assumptions and the two-parameter logistic model allows varying slopes. ... model with one item parameter. However, rather than being a particular IRT model, proponents of the model regard it as a model ... Specifically, in the original Rasch model, the probability of a correct response is modeled as a logistic function of the ... Linacre J.M. (2005). Rasch dichotomous model vs. One-parameter Logistic Model. Rasch Measurement Transactions, 19:3, 1032 Rasch ...
Christensen, Ronald (1997). Log-linear models and logistic regression. Springer Texts in Statistics (Second ed.). New York: ... Discrete Statistical Models with Social Science Applications. North Holland, 1980. Bishop, Y. M. M.; Fienberg, S. E.; Holland, ...
2010). "Logistic and Poisson Regression Models". Generalized Linear Models With Applications in Engineering and the Sciences ( ... Other generalized linear models such as the negative binomial model or zero-inflated model may function better in these cases. ... A Poisson regression model is sometimes known as a log-linear model, especially when used to model contingency tables. Negative ... This model is popular because it models the Poisson heterogeneity with a gamma distribution. Poisson regression models are ...
Srivastava, P.W.; Shukla, R. (2008-09-01). "A Log-Logistic Step-Stress Model". IEEE Transactions on Reliability. 57 (3): 431- ... When the appropriate model is not known in advance, or there exist multiple accepted models, the test must estimate what model ... When the model is known in advance the test only needs to identify the parameters for the model, however it is necessary to ... its parameters) One would then use a known model or attempt to fit a model to relate how each stress factor influenced the ...
Standard statistical models, such as those involving the categorical distribution and multinomial logistic regression, assume ... Christensen, Ronald (1997). Log-linear models and logistic regression. Springer Texts in Statistics (Second ed.). New York: ... and separate regression models (logistic regression, probit regression, etc.). As a result, the term "categorical variable" is ... The identity of a particular word (e.g., in a language model): One of V possible choices, for a vocabulary of size V. For ease ...
Raju, N. S., Steinhaus, S. D., Edwards, J. E., & DeLessio, J. (1991). A logistic regression model for personnel selection. ... Raju, N. S., & Guttman, I. (1965). A new working formula for the split-half reliability model. Educational and Psychological ... Goldman, S. H., & Raju, N. S. (1986). Recovery of one- and two-parameter logistic item parameters: An empirical study. ... Clemans, W. V., Lunneborg, C. E., & Raju, N. S. (2004). Professor paul horst's legacy: A differential prediction model for ...
Verhulst named the model a logistic function. Albert Allen Bartlett - a leading proponent of the Malthusian Growth Model ... A Malthusian growth model, sometimes called a simple exponential growth model, is essentially exponential growth based on the ... Minnesota Logistic Model from Steve McKelvey, Department of Mathematics, Saint Olaf College, Northfield, Minnesota Laws Of ... Malthusian models have the following form: P ( t ) = P 0 e r t {\displaystyle P(t)=P_{0}e^{rt}} where P0 = P(0) is the initial ...
Page 60, Google Books Tjur, Tue (2009). "Coefficients of determination in logistic regression models". American Statistician. ... where LM and L0 are the likelihoods for the model being fitted and the null model, respectively. The Cox and Snell index is ... In logistic regression analysis, there is no agreed upon analogous measure, but there are several competing measures each with ... Logistic regression will always be heteroscedastic - the error variances differ for each value of the predicted score. For each ...
Methods for fitting such models include logistic and probit regression. Several statistics can be used to quantify the quality ... O'Connell, A. A. (2006). Logistic Regression Models for Ordinal Response Variables. SAGE Publications. (Nonparametric ... It is also used as a quality measure of binary choice or ordinal regression (e.g., logistic regressions) and credit scoring ... for binary classification or prediction of binary outcomes including binary choice models in econometrics. ...
Naive Bayes (NB). Generalized linear model (GLM) for Logistic regression. Support Vector Machine (SVM). Decision Trees (DT). ... The code below illustrates a typical call to build a classification model: BEGIN DBMS_DATA_MINING.CREATE_MODEL ( model_name ... model_settings'); END; where 'credit_risk_model' is the model name, built for the express purpose of classifying future ... These operations include functions to create, apply, test, and manipulate data-mining models. Models are created and stored as ...
Asadabadi, M. R., Saberi, M., & Chang, E. (2017, July). Logistic informatics modelling using concept of stratification (CST). ... Ghildyal, A., & Chang, E. IT Governance and Benefit Models: Literature Review and Proposal of a Novel Approach. Asadabadi, M. R ...
Yu, Chian-Son; Li, Han-Lin (2000). "A robust optimization model for stochastic logistic problems". International Journal of ... A very popular model of local robustness is the radius of stability model: ρ ^ ( x , u ^ ) := max ρ ≥ 0 { ρ : u ∈ S ( x ) , ∀ u ... Modern robust optimization deals primarily with non-probabilistic models of robustness that are worst case oriented and as such ... The non-probabilistic (deterministic) model has been and is being extensively used for robust optimization especially in the ...
When the logistic regression model is used to model the case-control data and the odds ratio is of interest, both the ... ISBN 978-0-7817-5564-1. Prentice RL, Pyke R (1979). "Logistic disease incidence models and case-control studies". Biometrika. ...
ISBN 978-0-471-22618-5. Christensen, R. (1997). Log-Linear Models and Logistic Regression (2nd ed.). Springer. Petitjean, F.; ... The saturated model is the model that includes all the model components. This model will always explain the data the best, but ... Other possible models are the conditional equiprobability model and the mutual dependence model. Each log-linear model can be ... Log-linear analysis models can be hierarchical or nonhierarchical. Hierarchical models are the most common. These models ...
"Using Logistic Regression Modeling to Predict Sexual Recidivism". Sexual Abuse: A Journal of Research and Treatment. 24 (4): ...
Machine learning, Statistical models, Logistic regression, Regression models). ... Both employ essentially the same model but in different ways. In logistic regression one typically knows the parameters β i {\ ... The Bradley-Terry model is a probability model for the outcome of pairwise comparisons between individuals, teams, or objects. ... Ordinal regression Rasch model Scale (social sciences) Elo rating system Thurstonian model Hunter, David R. (2004). "MM ...
An Application of the Conditional Logistic Choice Model." Journal of Econometrics, vol. 121, no. 1-2: pp. 271-296. DOI: 10.1016 ...
Wang, Mingliang; Rennolls, Keith (2005). "Tree diameter distribution modelling: Introducing the logit-logistic distribution". ... The log-logistic distribution, also known as the Fisk distribution in economics, is a special case of the log metalog where b l ... The logit-logistic distribution is a special case of the logit metalog where a i = 0 {\displaystyle a_{i}=0} for all i > 2 {\ ... This ordering was chosen so that the first two terms in the resulting metalog quantile function correspond to the logistic ...
He is best known for the logistic growth model. Verhulst developed the logistic function in a series of three papers between ... Population dynamics Logistic map Logistic distribution Verhulst, Pierre-François (1838). "Notice sur la loi que la population ... Although the continuous-time logistic equation is often compared to the logistic map because of similarity of form, it is ... Published as:Cramer, J. S. (2004). "The early origins of the logit model". Studies in History and Philosophy of Science Part C ...
The CP-GEP model is a logistic regression model. A repeated nested cross-validation scheme (double loop cross validation) was ... The CP-GEP model was developed by the Mayo Clinic and SkylineDx BV, and it has been clinically validated in multiple studies. ... The CP-GEP model classifies patients as low or high risk for nodal metastasis based on patient age at melanoma biopsy (clinical ... The specific genes included in this CP-GEP model are MLANA, PLAT, ITGB3, SERPINE2, LOXL4, IL8, TGFBR1, and GDF15. The sample ...
... and 3 parameter logistic models as well as the partial credit model and generalized partial credit model. It can also generate ... 2 and 3 parameter logistic models, graded response models, partial credit and generalized partial credit models, rating scale ... Rasch dichotomous model vs. One-parameter Logistic Model [3]. Rasch Measurement Transactions [4], 2005, 19:3 p. 1032 "MIRT". ... IRTEQ supports various popular unidimensional IRT models: Logistic models for dichotomous responses (with 1, 2, or 3 parameters ...
Logistic model may refer to: Logistic function - a continuous sigmoidal curve Logistic map - a discrete version, which exhibits ... chaotic behavior Logistic regression This disambiguation page lists articles associated with the title Logistic model. If an ...
Logistic model is appropriate population growth model where ecosystems have limited resources putting a cap on the maximum ... Logistic population model is given by the differential equation , where k is a positive constant and K is the carrying capacity ... For more on growth models check my online book Flipped Classroom Calculus of Single Variable https://versal.com/learn/vh45au/ ...
... provided a general logistic-model-based estimator of the attributable fraction for case-control data, and Benichou and Gail ( ... Maximum likelihood estimation of the attributable fraction from logistic models Biometrics. 1993 Sep;49(3):865-72. ... 1985, American Journal of Epidemiology 122, 904-914) provided a general logistic-model-based estimator of the attributable ... estimator is not, however, the maximum likelihood estimator (MLE) based on the model, as it uses the model only to construct ...
The logistic model of a mental test was introduced by the present writer in Chapters 17 through 20 of Lord and Novick, ... Ability Distribution, Mathematical Models, Statistics, Test Theory Abstract. The logistic model of a mental test was introduced ... Statistical Theory for Logistic Mental Test Models With a Prior Distribution of Ability. Author(s):. Birnbaum, Allan ...
Endogeneity in logistic regression models. Emerg Infect Dis. 2005;11:503-504.PubMedGoogle Scholar ... Endogeneity in Logistic Regression Models. Volume 11, Number 3-March 2005. Article Views: 431. Data is collected weekly and ... Avery, G., Ethelberg, S., & Mølbak, K. (2005). Endogeneity in Logistic Regression Models. Emerging Infectious Diseases, 11(3), ... Avery G, Ethelberg S, Mølbak K. Endogeneity in Logistic Regression Models. Emerging Infectious Diseases. 2005;11(3):503-505. ...
Model-based Systems Engineering, Production and Logistics Systems Modeling, Simulation Optimization, SysML ... A Model-Driven Approach to Interoperability Between Simulation and Optimization for Production and Logistics Systems. ... Sprock, T. (2020), A Model-Driven Approach to Interoperability Between Simulation and Optimization for Production and Logistics ... Formal abstractions are linked to specialized system models to specify corresponding analysis models and tool interfaces. This ...
A day at Model Would you like to gain an insight into the world of logistics at Model? Marko, Sophie and Alexander take you on ... Model Services. We support your packaging and logistics processes with our services. ... Model Group - The Company. Here you will find all information, facts and background information about the Model Group. ... We are characterized by a collegial relationship, fun and enthusiasm for logistics, and close teamwork between logistics and ...
Diagnosing and revising logistic regression models: Effect on internal solitary wave propagation - Author: Chen‐Yuan Chen, ... fit and prediction ability of the revised logistic regression model are more appropriate than those of the original model. ... Diagnosing and revising logistic regression models: Effect on internal solitary wave propagation. Chen‐Yuan Chen (Department of ... Chen, C., Yang, H.P., Chen, C. and Chen, T. (2008), "Diagnosing and revising logistic regression models: Effect on internal ...
... an R package to estimate different types of Rasch models; Mair, Hatzinger, & Mair, 2014) functions to estimate the model and ... The model is applied to an English as a foreign language reading comprehension test and the results are discussed. ... The applications of the model in test validation, hypothesis testing, cross-cultural studies of test bias, rule-based item ... an extension of the Rasch model with linear constraints on item parameters, along with eRm ( ...
This chapter analyzes a thematic tourist park using a logarithmic model. The study shows that the application of the model ... Intelligent Touristic Logistics Model to Optimize Times at Attractions in a Thematic Amusement Park: 10.4018/978-1-7998-2112-0. ... "Intelligent Touristic Logistics Model to Optimize Times at Attractions in a Thematic Amusement Park." In Smart Systems Design, ... "Intelligent Touristic Logistics Model to Optimize Times at Attractions in a Thematic Amusement Park." Smart Systems Design, ...
Conceptualization of a Research Model for Sustainable Logistics Practices and Logistics Transport Performance. Publication Type ... HomePublicationsConceptualization of a Research Model for Sustainable Logistics Practices and Logistics Transport Performance ... Conceptualization of a Research Model for Sustainable Logistics Practices and Logistics Transport Performance. Journal ... Url : https://www.researchgate.net/publication/333401114_Conceptualization_of_a_Research_Model_for_Sustainable_Logistics_ ...
Your models involves 4 variables, with v having 2 levels. Work backwards from the logistic regression model. According to the ... Although every logistic regression model might have a corresponding log-linear model (Poisson regression with categorical ... Thus a log-linear model equivalent to a logistic regression model will include all interactions among the independent variables ... So the log-linear model corresponding to your logistic regression model is:. n ~ v + x + y + z + x:z + x:y + y:z + x:y:z + v:z ...
Putting logistics modelling and simulation into training practice essay from our essays database at Essays Bank. Browse more ... Logistics Logistics Logistics Logistics logistics using promodel logistics logistics Marketing And Logistics Reverse Logistics ... Logistics Cii Logistics logistics Logistics Logistics Putting logistics modelling and simulation into training practice ... and tools used in ESAN to manage the functional requirements of the logistics training system and trying that some forms of ...
Investigating nonlinear speculation in cattle, corn, and hog futures markets using logistic smooth transition regression models ... Investigating nonlinear speculation in cattle, corn, and hog futures markets using logistic smooth transition regression models ... Investigating nonlinear speculation in cattle, corn, and hog futures markets using logistic smooth transition regression models ... Using smooth transition regression models, we find a similar structure of nonlinearities with regard to the number of different ...
Aggregate Industries has announced changes in the logistics model for its concrete products business. ... Aggregate Industries changes logistics model for concrete products business. Save to read list. Published by Lucy Stewardson, ... www.worldcement.com/europe-cis/13032019/aggregate-industries-changes-logistics-model-for-concrete-products-business/ ... Aggregate Industries has changed the logistics model for its concrete products business. ...
An ordered logistic regression model was used to examine factors that worsen the car accident level. A total sample of 385 car ... The model estimation result showed that being experienced drivers (Coef. = 0.686; p-value, = 0.050) were found to increase the ... Identification of Determinant Factors for Car Accident Levels Occurred in Mekelle City, Tigray, Ethiopia: Ordered Logistic ...
The simulation modelling demonstrates that LHC can significantly benefit the logistics efficiency in terms of capacity ... Logistics horizontal collaboration:an agent-based simulation approach to model collaboration dynamics ... Zhu, Jie (2017) Logistics horizontal collaboration:an agent-based simulation approach to model collaboration dynamics. PhD ... Logistics horizontal collaboration (LHC) is believed to be an innovative approach to tackle the increasing logistics challenges ...
... outbreak in India is done by using the logistic growth model and the Susceptible-Infectious-Recovered (SIR) framework. ... The mathematical modelling of the Coronavirus disease (COVID-19) ... outbreak in India is done by using the logistic growth model ... Mathematical Modelling of Coronavirus disease (COVID-19) Outbreak in India using Logistic Growth and SIR Models ... The parameters of the models are estimated by utilizing real-time data. The models predict the ending of the pandemic in these ...
The model is developed based on reported experiences of several best practice organizations. The central theme of the paper ... This paper describes a proposed model presenting the implementation of SAP R/3 from an integrative and holistic perspective. ... Al‐Mashari, M., & Zairi, M. (2000). The effective application of SAP R/3: a proposed model of best practice. Logistics ... "The effective application of SAP R/3: a proposed model of best practice." Logistics Information Management 13.3 (2000): 156-166 ...
Sunset Speedway is proud to announce that Bridgeport Logistics, LLC has become the 2 ... Bridgeport Logistics LLC. 16520 SW 72nd Ave Portland, Or 97224. 503-620-6977. Transportation logistics ... "Bridgeport Logistics, LLC. is excited to partner with Sunset Speedway for the 2016 season. We were looking for a new way to ... Bridgeport Logistics, LLC was founded in 2003 by a group of committed professionals with combined 100+ years of industry ...
A Note on Stability of Stochastic Logistic Model by Incorporating the Ornstein-Uhlenbeck Process ... A Note on Stability of Stochastic Logistic Model by Incorporating the Ornstein-Uhlenbeck Process. * Tawfiqullah Ayoubi ... This result is verified via several examples in Appendix A. Besides; we prove that the stochastic logistic model, by ... In this research, we first prove that the stochastic logistic model (10) has a positive global solution. Subsequently, we ...
Matsouaka, Roland A. and Tchetgen Tchetgen, Eric J., "Likelihood Based Estimation of Logistic Structural Nested Mean Models ... Likelihood Based Estimation of Logistic Structural Nested Mean Models with an Instrumental Variable ...
If the male and female univariate models and interaction model are run using a logit model, you can see that the interaction ... In this model, that translates into forcing the logit to zero for the no treatment and male condition (value of zero for the ... Stata Logit Model: Dummy Interaction With/Without Dropping Intercept vs Sub-Group Odds Ratio. Ask Question ... So I would do something like this, which leads to identical conclusions any way you do it (as long as the model stays fully ...
... as the traditional frequentist logistic regression model, but provides more flexibility in model updating. That is, EXPLORER ... "EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning." J Biomed Inform ... "EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning." J Biomed Inform ... EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning.. Publication , ...
In the model on 60-day mortality in sepsis and COVID-19 there were significant interactions with disease group for age, sex and ... In the model on 60-day mortality in ARDS and COVID-19 significant interactions with cohort were found for acute disease ... in logistic regression models. We included 32,501 adult ICU patients. ... Logistic modelling. The interaction between the disease group and the individual risk factors in a logistic regression was used ...
Logistics Library software you can quickly build realistic simulation models of dynamic warehousing and logistics processes. ... Logistics Library software you can quickly build realistic simulation models of dynamic warehousing and logistics processes. ... Warehousing and logistics nowadays is much more than just "moving boxes". Warehouses are located in a dynamic environment of ... The Warehousing & Logistics Library offers the functionality to simulate all facets of a warehouse. In addition to the standard ...
Logistics, Supply Chain, and Transportation Modeling with ExtendSim. *Acquisition, sustainability, integrated logistics, ... However, the combined use of discrete rate and discrete event modeling in mescoscopic models can represent logistics flow ... Logistics (IBSAL) model developed in ExtendSim. This biomass supply chain model takes into consideration the logistical ... This paper describes the framework development of a dynamic integrated biomass supply analysis and logistics model (IBSAL) to ...
Tag Archives: logistic model Math and Religion This was a catchy, misleading title that I could not resist, since my essay is ... This entry was posted in Math Inquiries and tagged Bart Ehrman, exponential growth, logistic model, religion, Rodney Stark on ... Ladies Diary Lambert law of inertia Leonhard Euler Lewis Carroll limits linear transformations logarithm logic logistic model ... So I thought I would clarify the math and also offer some variations on the models, which eventually reflected the actual ...
  • In a logistic regression analysis, we would come up with some magical cutoff point, say, 30 days, and anyone who canceled within 30 days would be considered a case of churn related to that customer complaint, while a cancellation after 30 days wouldn't be considered churn. (salesforce.com)
  • Logistic regression analysis (corrected for extent of PC) shows RFS (HR = 1.24 (95% CI: 0.75-2.05), P = 0.39) and OS (HR = 1.37 (95% CI: 0.74-2.54), P = 0.32) are not significantly different. (lu.se)
  • Iron-rich food consumption and associated factors among children aged 6-23 months in Sierra Leone: multi-level logistic regression analysis. (bvsalud.org)
  • A case-control logistic regression analysis of risk factors (104 cases and 412 controls) showed family history, wearing shoes during childhood, obesity and urban residence were significantly associated with flat foot. (who.int)
  • The goodness‐of‐fit and prediction ability of the revised logistic regression model are more appropriate than those of the original model. (emerald.com)
  • Survey data was used to develop univariate and multivariate logistic regression models for six outcome variables originating from the items assessing the acceptance of specific types of eHealth applications. (springer.com)
  • However, when combined in multivariate models, only the belief in the usefulness of the Internet (five of six models), level of education (four of six models), and previous hospitalization due to chronic disease (three of six models) maintained the effect on the independent variables. (springer.com)
  • To examine the relative importance of these factors, CDC used data from the 1989-1991 National Health Interview Survey (NHIS) and a multivariate model to estimate the independent effect of each factor on self-reported arthritis. (cdc.gov)
  • Multivariate logistic regression was used to assess the relation between self-reported arthritis and age, race, ethnicity, education, and BMI. (cdc.gov)
  • In statistics , multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems , i.e. with more than two possible discrete outcomes. (wikipedia.org)
  • Multinomial logistic regression is known by a variety of other names, including polytomous LR , [2] [3] multiclass LR , softmax regression , multinomial logit ( mlogit ), the maximum entropy ( MaxEnt ) classifier, and the conditional maximum entropy model . (wikipedia.org)
  • Multinomial logistic regression is used when the dependent variable in question is nominal (equivalently categorical , meaning that it falls into any one of a set of categories that cannot be ordered in any meaningful way) and for which there are more than two categories. (wikipedia.org)
  • Multinomial logistic regression is a particular solution to classification problems that use a linear combination of the observed features and some problem-specific parameters to estimate the probability of each particular value of the dependent variable. (wikipedia.org)
  • If the multinomial logit is used to model choices, it relies on the assumption of independence of irrelevant alternatives (IIA), which is not always desirable. (wikipedia.org)
  • It can range from (typically) being a Binomial Logistic Regression Algorithm to being a Multinomial Logistic Regression Algorithm . (gabormelli.com)
  • This course will cover a broad family of GLMs, including binary, multinomial, ordered, and conditional logistic regression models, as well as models designed for count data (Poisson regression and negative binomial models). (ecpr.eu)
  • If the male and female univariate models and interaction model are run using a logit model, you can see that the interaction term is not signif (P=0.184), but if the constant is left out the interaction term is significant - however, that interaction is biased by the constant term - obviously. (stackexchange.com)
  • I actually favor the interaction model with the constant term, since the slope difference between the male and female treatment effects (univariate models) can be discerned. (stackexchange.com)
  • Univariate logistic regression models developed for six types of eHealth solutions demonstrated their higher acceptance among younger respondents, living in urban areas, who have attained a higher level of education, used the Internet on their own, and were more confident about its usefulness in making health-related decisions. (springer.com)
  • Case studies are initially conducted to examine the key elements which can support the design of LHC, and to make a classification of models for collaboration. (lancs.ac.uk)
  • A Logistic Model Fitting Algorithm is a discriminative maximum entropy-based generalized linear classification algorithm that accepts a logistic model family . (gabormelli.com)
  • It can (typically) be represented as a Generalized Linear Model (a linear classifier that minimizes the classification error based on the sum of differences). (gabormelli.com)
  • You're looking for a complete Classification modeling course that teaches you everything you need to create a Classification model in Python, right? (udemy.com)
  • You've found the right Classification modeling course! (udemy.com)
  • Identify the business problem which can be solved using Classification modeling techniques of Machine Learning. (udemy.com)
  • Create different Classification modelling model in Python and compare their performance. (udemy.com)
  • This course teaches you all the steps of creating a classification model, which is the most popular Machine Learning model, to solve business problems. (udemy.com)
  • [1] That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable , given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc. (wikipedia.org)
  • This allows the choice of K alternatives to be modeled as a set of K -1 independent binary choices, in which one alternative is chosen as a "pivot" and the other K -1 compared against it, one at a time. (wikipedia.org)
  • an Iteratively Reweighted Least Squares (IRLS)-based Binary Logistic Regression Algorithm . (gabormelli.com)
  • Increasingly, logistic regression methods for genetic association studies of binary phenotypes must be able to accommodate data sparsity, which arises from unbalanced case-control ratios and/or rare genetic variants. (karger.com)
  • To identify factors associated with the development of Pulmonary embolism, a multivariable Binary Logistic Regres- sion model with sensitivity analysis was run. (who.int)
  • To assess the association between park access and HBP, we built multilevel logistic models to account for variation in HBP by zip code. (cdc.gov)
  • A multilevel logistic regression model was employed to identify associated factors. (bvsalud.org)
  • The general rule is the model should contain the $N-1$ and lower order interactions between the independent variables, and for every term in the logistic model formula, an interaction between in and the dependent variable. (stackexchange.com)
  • You will learn how to run a regression model when the dependent variable is not a continuous numerical one. (ecpr.eu)
  • Logistic regression, alternatively, has a dependent variable with only a limited number of possible values. (salesforce.com)
  • The mathematical modelling of the Coronavirus disease (COVID-19) outbreak in India is done by using the logistic growth model and the Susceptible-Infectious-Recovered (SIR) framework. (researchsquare.com)
  • Neither Rodney Stark nor Bart Ehrman described explicitly the underlying mathematical models of exponential growth that they were using and exactly what was meant by a rate of growth. (josmfs.net)
  • This equation is the best mathematical model to describe the law of population growth under the condition of limited resources. (programmer.ink)
  • This paper presents a model-driven approach to integrating simulation and optimization methods by exchanging formal system models and analysis abstractions between them, defined in SysML. (nist.gov)
  • This approach (model-driven system-analysis integration) is demonstrated by developing a multi-fidelity, multi-method simulation optimization methodology and applying it to a supply chain design case study. (nist.gov)
  • Link Simulation & Training, change our connected image with partners companies selected by projects involvement together, but also as a provider of logistics training services ranging from staff training systems development to simulation and training research. (essaysbank.com)
  • These are followed by Agent-Based Simulation to model a typical collaboration process and work out what benefits would emerge if participating in horizontal collaboration and how the collaboration can produce the impacts on the supply chain operations for individuals and the system as a whole. (lancs.ac.uk)
  • The simulation modelling demonstrates that LHC can significantly benefit the logistics efficiency in terms of capacity utilization and customer service in the sense of order fill-rate, and such beneficial effects are consistently observed in different supply chain environments. (lancs.ac.uk)
  • With the Tecnomatix® Warehousing & Logistics Library software you can quickly build realistic simulation models of dynamic warehousing and logistics processes. (cardsplmsolutions.com)
  • Their models improve the quality of decisions, identifies bottlenecks, and assists with investment strategy for their clients by providing quantitative decision support and systems analysis through discrete event simulation. (extendsim.com)
  • With this simulation model, you can optimize vehicle fleet mix and develop the best mine-to-processing-plant ore transportation plan. (focus-grp.com)
  • Focus Group worked as a sub-contractor for simulation modeling, initial data preparation and analysis of the modeling results. (focus-grp.com)
  • In this research, we first prove that the stochastic logistic model (10) has a positive global solution. (ccsenet.org)
  • we prove that the stochastic logistic model, by incorporating the Ornstein-Uhlenbeck process is stable in zero solution. (ccsenet.org)
  • We use a combined modeling approach guided by gene expression classifier methods that infers a time-series of stochastic commitment events from experimental growth characteristics and gene expression profiling of individual hematopoietic cells captured immediately before and after commitment. (lu.se)
  • Against this background, we develop a Monte Carlo time-series stochastic model of transcription where the parameters governing promoter status, mRNA production and mRNA decay in multipotent cells are fitted to experimental static gene expression distributions. (lu.se)
  • The model estimation result showed that being experienced drivers (Coef. (ajol.info)
  • Matsouaka, Roland A. and Tchetgen Tchetgen, Eric J., "Likelihood Based Estimation of Logistic Structural Nested Mean Models with an Instrumental Variable" (August 2014). (bepress.com)
  • a Maximum Likelihood Estimation (MLE)-based Logistic Regression Algorithm . (gabormelli.com)
  • To investigate the performance of diffusion-weighted (DW) MRI with mono-, bi- and stretched-exponential models in predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) for breast cancer, and further outline a predictive model of pCR combining DW MRI parameters, contrast-enhanced (CE) MRI findings, and/or clinical-pathologic variables. (springer.com)
  • Quantitative DW imaging parameters were computed according to the mono-exponential (apparent diffusion coefficient [ADC]), bi-exponential (pseudodiffusion coefficient and perfusion fraction), and stretched-exponential (distributed diffusion coefficient and intravoxel heterogeneity index) models. (springer.com)
  • Logistic population model is given by the differential equation , where k is a positive constant and K is the carrying capacity. (geogebra.org)
  • Logistic regression models a function of the mean of a Bernoulli distribution as a linear equation (the mean being equal to the probability $p$ of a Bernoulli event ). (gabormelli.com)
  • If the species has natural enemies, food, space and other resources in this ecosystem and is also insufficient (non ideal environment), the growth function meets the logistic equation, and the image is S-shaped. (programmer.ink)
  • In the following contents, the principle, ecological significance and application of logistic equation will be introduced in detail. (programmer.ink)
  • This kind of collaborative logistics is quickly gaining momentum in practice but relevant contributions in literature are scarce. (lancs.ac.uk)
  • The model is developed based on reported experiences of several best practice organizations. (deepdyve.com)
  • begingroup$ It might help if you showed the structure and results of the 2 models. (stackexchange.com)
  • begingroup$ Interactions in non-linear models can be tricky. (stackexchange.com)
  • For example, the simultaneous equations approach, such as that outlined by Greene ( 7 ), would have used predicted values of bloody diarrhea from the first stage of the model as instrumental variables for the actual value in the model for hemolytic uremic syndrome. (cdc.gov)
  • This study aims to apply a systematic statistical approach, including several plot indexes, to diagnose the goodness of fit of a logistic regression model, and then to detect the outliers and influential observations of the data from experimental data. (emerald.com)
  • Logistics horizontal collaboration (LHC) is believed to be an innovative approach to tackle the increasing logistics challenges. (lancs.ac.uk)
  • To compete in dynamic markets, a rational design approach to warehouse and logistics processes is of great importance. (cardsplmsolutions.com)
  • The beauty of the Bayesian approach is that fitting the model can be factored from the decision theory. (mc-stan.org)
  • After working with this model more, I've realized there's an additional complexity to my approach that I ignored. (mc-stan.org)
  • Logistic regression is an important machine learning algorithm. (gabormelli.com)
  • Statistics.com) ⇒ http://www.statistics.com/ourcourses/logistic/ Retrieved:2023-11-12. (gabormelli.com)
  • Additionally, the correlation between the dependent variables can create significant multicollinearity, which violates the assumptions of standard regression models and results in inefficient estimators. (cdc.gov)
  • Intuitively searching for the model that makes the fewest assumptions in its parameters. (gabormelli.com)
  • Since most supply chains are built to send items out - not bring them back in - reverse logistics is a new and daunting challenge for many organizations. (manufacturing.net)
  • However, organizations cannot model a reverse logistics supply chain individually. (manufacturing.net)
  • 20% - Prepare inputs for predictive model performance. (sas.com)
  • Logistic model may refer to: Logistic function - a continuous sigmoidal curve Logistic map - a discrete version, which exhibits chaotic behavior Logistic regression This disambiguation page lists articles associated with the title Logistic model. (wikipedia.org)
  • Intelligent Touristic Logistics Model to Optimize Times at Attractions in a Thematic Amusement Park. (igi-global.com)
  • Define the logistic regression formula and the loss function to optimize. (devhubby.com)
  • With most buildings designed specifically for forward logistics, businesses must optimize their supply chain to improve the customer experience in both directions. (manufacturing.net)
  • The differential formula of logistic model is: dx/dt=rx(1-x), in which r is the rate parameter. (programmer.ink)
  • We support your packaging and logistics processes with our services. (modelgroup.com)
  • The results of this study clearly show that the presence of bloody diarrhea is an endogenous variable in the model showing predictors of hemolytic uremic syndrome, in that the diarrhea is shown to be predicted by, and therefore strongly correlated with, several other variables used to predict hemolytic uremic syndrome. (cdc.gov)
  • The underlying problem in the study is the theoretical specifications for the model, in which genotypes, strains, and symptoms are mixed, despite reasonable expectations that differences in 1 level may predict differences in another. (cdc.gov)
  • The models predict the ending of the pandemic in these states and estimate the number of people that would be affected under the prevailing conditions. (researchsquare.com)
  • A logistic regression is a way to predict the probability of something happening. (salesforce.com)
  • That's the primary reason you shouldn't use logistic regression and why I urge customers to always predict a number that directly impacts how they will act on information, not information for the sake of information, but information that leads to ROI. (salesforce.com)
  • I have the loglinear model with parameters x, y, z, v, x y, x v, and z*v. As far as i understand there should exist a logistic regression model that essentially is equivalent to this, using v as response variable. (stackexchange.com)
  • I end up with the parameters x, z and x*y for the logistic regression model which turns out to be incorrect when testing in R. (stackexchange.com)
  • I have also tried many other combinations of parameters in R but neither of the parameters in these models has the same values as the parameters in my loglinear model. (stackexchange.com)
  • The parameters of the models are estimated by utilizing real-time data. (researchsquare.com)
  • The model considers production plans, extracted raw materials, and infrastructure facilities, as well as the parameters of all equipment types. (focus-grp.com)
  • Define the variables for the model parameters, which include the weights and the bias. (devhubby.com)
  • The relative importance of risk factors for 60-day mortality was evaluated using the interaction with disease group (Sepsis, ARDS or COVID-19) in logistic regression models. (nature.com)
  • 1985, American Journal of Epidemiology 122, 904-914) provided a general logistic-model-based estimator of the attributable fraction for case-control data, and Benichou and Gail (1990, Biometrics 46, 991-1003) gave an implicit-delta-method variance formula for this estimator. (nih.gov)
  • This finding indicates that although this model fits the data, it has a slight overdispersion. (emerald.com)
  • After three outliers and influential observations (cases 11, 27, and 49) are removed from the data, and the remaining observations are refitted the goodness‐of‐fit of the revised model to the data is improved. (emerald.com)
  • Inputing data from a sampling of time, they used ExtendSim to model the different systems that were supplying and demanding material from bay locations at various rates, quantities, and shifts of operation. (extendsim.com)
  • The goal is to model the probability of a random variable Y being 0 or 1 given experimental data. (gabormelli.com)
  • Prepare the data for training the model. (devhubby.com)
  • second, the estimated m is used for log- F -penalized logistic regression analyses of all variants using data augmentation with standard software. (karger.com)
  • Detecting patterns of occupational illness clustering with alternating logistic regressions applied to longitudinal data. (cdc.gov)
  • For such cluster-correlated longitudinal data, alternating logistic regressions may be used to model the pattern of occupational illness clustering. (cdc.gov)
  • Probability of commitment in time is a function of gene expression as defined by a logistic regression model obtained from experimental single-cell expression data. (lu.se)
  • The dataset is relatively small, and the authors use stepwise logistic regression models to detect small differences. (cdc.gov)
  • In this video, we'll use the dataset framingham (CSV) to build a logistic regression model. (mit.edu)
  • Models using different BMI categories and models run without proxy-reported observations yielded similar findings. (cdc.gov)
  • In addition to the standard functionalities, such as storage racks, layout and reach trucks, the program also has functions for order picking and value added logistics. (cardsplmsolutions.com)
  • The applications of the model in test validation, hypothesis testing, cross-cultural studies of test bias, rule-based item generation, and investigating construct irrelevant factors which contribute to item difficulty are explained. (ed.gov)
  • This paper presents an illustration of the integration of cognitive psychology and psychometric models to determine sources of item difficulty in an Arithmetic Test (AT), constructed by the authors, by means of its analysis with the LLTM. (bvsalud.org)
  • This is exactly the situation in your logistic regression, with Y corresponding to your v, the B:C interaction corresponding to your x:y interaction, and A corresponding to your z. (stackexchange.com)
  • Several collaborators like the last logit model without the constant term, since it yields a significant interaction term -- like the female sub-group analysis. (stackexchange.com)
  • Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Logistic_regression#Model_fitting Retrieved:2020-9-6. (gabormelli.com)
  • Describir las repercusiones generadas en el área de la salud mental en la población uruguaya mayor de 18 años, GC1, JH3, DA4: study de las variables ansiedad, tristeza y dificultades para conciliar el sueño, en el periodo comprendido entre el 13 de marzo de conception, literature search, 2020 al 10 de junio de 2021. (bvsalud.org)
  • An often overlooked problem in building statistical models is that of endogeneity, a term arising from econometric analysis, in which the value of one independent variable is dependent on the value of other predictor variables. (cdc.gov)
  • Formal abstractions are linked to specialized system models to specify corresponding analysis models and tool interfaces. (nist.gov)
  • Baosteel Technology Center/Automation Institute , the largest iron and steel company in China and a global 500 company, used ExtendSim in a melt iron transportation logistics analysis project. (extendsim.com)
  • a Generative Model Training Algorithm , such as a linear discriminant analysis . (gabormelli.com)
  • And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. (udemy.com)
  • This kind of analysis is very common in academia, but after 10 years of doing analyses at hundreds of companies, in dozens of industries, I have never found a case where it the logistic model made sense for business operations to use directly. (salesforce.com)
  • In this paper, a comparative analysis of alternating logistic regressions with generalized estimating equations and random-effects logistic regression is presented, and the relative strengths of the three methods are discussed. (cdc.gov)
  • In addition to resulting in multi-million dollar savings, the model assesses a range of trade growth scenarios. (extendsim.com)
  • Within our system, we identify robust model solutions for the multipotent population within physiologically reasonable values and explore model predictions with regard to molecular scenarios of entry into commitment. (lu.se)
  • Logistic regression in Python tutorial for beginners. (udemy.com)
  • You can do Predictive modeling using Python after this course. (udemy.com)
  • An ordered logistic regression model was used to examine factors that worsen the car accident level. (ajol.info)
  • They then select historical crisis periods and, using a logistic regression framwork, examine whether the three sub-indexes are associated with being in a crisis period or a normal period. (federalreserve.gov)
  • You will learn practical skills related to running GLMs, including proper interpretation of the regression outcome and presentation of model results in the form of graphs and tables. (ecpr.eu)
  • In order to reach this aim, a group of operations required to solve the items of the test were proposed, the dimensionality was evaluated, and the goodness of fit of items to both the Rasch and the LLTM models was studied. (bvsalud.org)
  • Because the strain is in part determined by the presence of these toxins, including both strain and genotype in the model means that the standard errors for variables for the Shiga-containing strains and bloody diarrhea symptom are likely to be too high, and hence the significance levels (p values) obtained from the regression models are higher than the true probability because of a type I error. (cdc.gov)
  • The model suggests distinct dependencies of different commitment-associated genes on mRNA dynamics and promoter activity, which globally influence the probability of lineage commitment. (lu.se)
  • It is quite common in social sciences to want to model respondents' choices between two or more categories, measuring answers on an ordinal scale or event counts. (ecpr.eu)
  • 1. Introduction This document describes (briefly) the processes, methodology, and tools used in ESAN to manage the functional requirements of the logistics training system and trying that some forms of learning attempt to get close as possible to the real situation. (essaysbank.com)
  • Using ExtendSim to build virtual integrated (operations and logistics) models that consider all physical intermodal processes to measure the actions, effects, and responses within a system, WorleyParsons is able to validate design, assess sensitivities, and quantify operational risk to project - not just local risks to each process, but all interconnected risks through the logistics chain. (extendsim.com)
  • Overall, the project allows for companies to stay updated and integrate sustainability in their business plan, business model and daily operations. (lu.se)
  • It can use an Unconstrained Optimization Algorithm to maximize the log-likelihood of the logistic regression model (such as Newton-Raphson ). (gabormelli.com)
  • Logistics of container handling to improve system efficiency. (extendsim.com)
  • Although every logistic regression model might have a corresponding log-linear model (Poisson regression with categorical variables), the converse doesn't necessarily hold. (stackexchange.com)
  • By using the logit link as a function of the mean ($p$), the logarithm of the odds (log-odds) can be derived analytically and used as the response of a so-called generalised linear model . (gabormelli.com)
  • This course is an introduction to General Linear Models (GLMs). (ecpr.eu)
  • Do you understand the difference between logistic regression and linear regression? (salesforce.com)
  • In almost all cases, the linear model is better than the logistic model. (salesforce.com)
  • estimator is not, however, the maximum likelihood estimator (MLE) based on the model, as it uses the model only to construct the relative risk estimates, and not the covariate-distribution estimate. (nih.gov)
  • I would expect that for example the parameter x*v in the loglinear model would have equivalent estimate and variance as the x parameter in the logistic regression model, however this is not the case. (stackexchange.com)
  • The authors note that they excluded variables from the final model if the significance in initial models for those variables was less than an α level (p value) of 0.05. (cdc.gov)
  • A model will prove to be equally attractive and distinctive when parked up within any model truck collection! (search-impex.co.uk)
  • Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection, etc.) as the traditional frequentist logistic regression model, but provides more flexibility in model updating. (duke.edu)
  • Logistic model is appropriate population growth model where ecosystems have limited resources putting a cap on the maximum sustainable population, also known as carrying capacity. (geogebra.org)
  • The model is applied to an English as a foreign language reading comprehension test and the results are discussed. (ed.gov)
  • is defined as the multiple correlation coefficient for the model X 1 = f(X 2 ,X 3 ,…), and all X i are independent variables in the larger model ( 3 , 4 ). (cdc.gov)
  • However, bloody diarrhea is not the only endogenous variable in their models, and extensive modeling would be necessary to isolate the independent effects of the various predictor variables. (cdc.gov)
  • A suitable logistic regression model in which the relationship between the response variable and the explanatory variables is found. (emerald.com)
  • Your models involves 4 variables, with v having 2 levels. (stackexchange.com)
  • Initialize the variables and create a session for training the model. (devhubby.com)
  • It can be implemented by a Logistic Regression System (that solves a logistic function fitting task to produce a fitted logistic function ). (gabormelli.com)
  • The least square method is used for fitting, and the least square method is used to fit the logistic growth function. (programmer.ink)
  • Integrating the two presents a number of conceptual and technical problems, which can be overcome in a specific domain using formal system models. (nist.gov)
  • The logistic model of a mental test was introduced by the present writer in Chapters 17 through 20 of Lord and Novick, Statistical Theories of Mental Test Scores, where statistical inference methods were developed without assumption of a prior distribution of ability. (ets.org)
  • With the model, you can test if logistics structures can accommodate raw materials supply. (focus-grp.com)
  • The new model offers improved delivery performance and increased flexibility to make life easier for the customer. (worldcement.com)
  • In particular, LHC can produce better logistics performance in a relationship-based supply chain network where downstream customers can support upstream shippers with more stable and predictable demand. (lancs.ac.uk)
  • Measuring model performance. (sas.com)
  • 25% - Measure model performance. (sas.com)
  • Evaluate the performance of the model by comparing the predicted output labels with the actual output labels. (devhubby.com)
  • Bridgeport Logistics and Distribution provides a full line of transportation services throughout the continental United States. (sunsetspeedwaypark.com)