Confidence Intervals: 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.Risk Factors: 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.Odds Ratio: 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.Case-Control Studies: 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.Cohort Studies: 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.Prospective Studies: 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.Incidence: 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.Follow-Up Studies: 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.Logistic Models: 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.Treatment Outcome: 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.Time Factors: Elements of limited time intervals, contributing to particular results or situations.Retrospective Studies: 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.Proportional Hazards Models: Statistical models used in survival analysis that assert that the effect of the study factors on the hazard rate in the study population is multiplicative and does not change over time.Risk: The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.Multivariate Analysis: 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.Risk Assessment: 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)United StatesPrevalence: 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.Age Factors: 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.Cross-Sectional Studies: 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.Prognosis: 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.Pregnancy: The status during which female mammals carry their developing young (EMBRYOS or FETUSES) in utero before birth, beginning from FERTILIZATION to BIRTH.Questionnaires: 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.Infant, Newborn: An infant during the first month after birth.Smoking: Inhaling and exhaling the smoke of burning TOBACCO.Predictive Value of Tests: 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.Survival Analysis: 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.Genotype: The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.Randomized Controlled Trials as Topic: Works about clinical trials that involve at least one test treatment and one control treatment, concurrent enrollment and follow-up of the test- and control-treated groups, and in which the treatments to be administered are selected by a random process, such as the use of a random-numbers table.Genetic Predisposition to Disease: A latent susceptibility to disease at the genetic level, which may be activated under certain conditions.Registries: The systems and processes involved in the establishment, support, management, and operation of registers, e.g., disease registers.Regression Analysis: 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.Sex Factors: 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.Survival Rate: The proportion of survivors in a group, e.g., of patients, studied and followed over a period, or the proportion of persons in a specified group alive at the beginning of a time interval who survive to the end of the interval. It is often studied using life table methods.Double-Blind Method: A method of studying a drug or procedure in which both the subjects and investigators are kept unaware of who is actually getting which specific treatment.Kaplan-Meier Estimate: A nonparametric method of compiling LIFE TABLES or survival tables. It combines calculated probabilities of survival and estimates to allow for observations occurring beyond a measurement threshold, which are assumed to occur randomly. Time intervals are defined as ending each time an event occurs and are therefore unequal. (From Last, A Dictionary of Epidemiology, 1995)Breast Neoplasms: Tumors or cancer of the human BREAST.Polymorphism, Genetic: 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.Sensitivity and Specificity: 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)Longitudinal Studies: Studies in which variables relating to an individual or group of individuals are assessed over a period of time.Chi-Square Distribution: 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.Age Distribution: The frequency of different ages or age groups in a given population. The distribution may refer to either how many or what proportion of the group. The population is usually patients with a specific disease but the concept is not restricted to humans and is not restricted to medicine.Confounding Factors (Epidemiology): Factors that can cause or prevent the outcome of interest, are not intermediate variables, and are not associated with the factor(s) under investigation. They give rise to situations in which the effects of two processes are not separated, or the contribution of causal factors cannot be separated, or the measure of the effect of exposure or risk is distorted because of its association with other factors influencing the outcome of the study.European Continental Ancestry Group: Individuals whose ancestral origins are in the continent of Europe.Polymorphism, Single Nucleotide: A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.DenmarkSwedenReproducibility of Results: 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.Hospitalization: The confinement of a patient in a hospital.Recurrence: The return of a sign, symptom, or disease after a remission.Body Mass Index: 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)Cause of Death: Factors which produce cessation of all vital bodily functions. They can be analyzed from an epidemiologic viewpoint.Sex Distribution: The number of males and females in a given population. The distribution may refer to how many men or women or what proportion of either in the group. The population is usually patients with a specific disease but the concept is not restricted to humans and is not restricted to medicine.Myocardial Infarction: NECROSIS of the MYOCARDIUM caused by an obstruction of the blood supply to the heart (CORONARY CIRCULATION).Socioeconomic Factors: Social and economic factors that characterize the individual or group within the social structure.JapanComorbidity: 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.Biological Markers: 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.Cardiovascular Diseases: Pathological conditions involving the CARDIOVASCULAR SYSTEM including the HEART; the BLOOD VESSELS; or the PERICARDIUM.China: A country spanning from central Asia to the Pacific Ocean.HIV Infections: Includes the spectrum of human immunodeficiency virus infections that range from asymptomatic seropositivity, thru AIDS-related complex (ARC), to acquired immunodeficiency syndrome (AIDS).Drug Administration Schedule: Time schedule for administration of a drug in order to achieve optimum effectiveness and convenience.Population Surveillance: Ongoing scrutiny of a population (general population, study population, target population, etc.), generally using methods distinguished by their practicability, uniformity, and frequently their rapidity, rather than by complete accuracy.Linear Models: 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.Occupational Exposure: The exposure to potentially harmful chemical, physical, or biological agents that occurs as a result of one's occupation.Stroke: A group of pathological conditions characterized by sudden, non-convulsive loss of neurological function due to BRAIN ISCHEMIA or INTRACRANIAL HEMORRHAGES. Stroke is classified by the type of tissue NECROSIS, such as the anatomic location, vasculature involved, etiology, age of the affected individual, and hemorrhagic vs. non-hemorrhagic nature. (From Adams et al., Principles of Neurology, 6th ed, pp777-810)Disease Progression: The worsening of a disease over time. This concept is most often used for chronic and incurable diseases where the stage of the disease is an important determinant of therapy and prognosis.Colorectal Neoplasms: Tumors or cancer of the COLON or the RECTUM or both. Risk factors for colorectal cancer include chronic ULCERATIVE COLITIS; FAMILIAL POLYPOSIS COLI; exposure to ASBESTOS; and irradiation of the CERVIX UTERI.Great BritainSeverity of Illness Index: Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.CaliforniaAsian Continental Ancestry Group: Individuals whose ancestral origins are in the southeastern and eastern areas of the Asian continent.Epidemiologic Methods: Research techniques that focus on study designs and data gathering methods in human and animal populations.Environmental Exposure: The exposure to potentially harmful chemical, physical, or biological agents in the environment or to environmental factors that may include ionizing radiation, pathogenic organisms, or toxic chemicals.Electrocardiography: Recording of the moment-to-moment electromotive forces of the HEART as projected onto various sites on the body's surface, delineated as a scalar function of time. The recording is monitored by a tracing on slow moving chart paper or by observing it on a cardioscope, which is a CATHODE RAY TUBE DISPLAY.Drug Therapy, Combination: Therapy with two or more separate preparations given for a combined effect.ROC Curve: 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.Mortality: All deaths reported in a given population.Lung Neoplasms: Tumors or cancer of the LUNG.Neoplasms: New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms.African Americans: Persons living in the United States having origins in any of the black groups of Africa.Alcohol Drinking: Behaviors associated with the ingesting of alcoholic beverages, including social drinking.FinlandAnalysis of Variance: A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.Probability: The study of chance processes or the relative frequency characterizing a chance process.ItalyHealth Surveys: A systematic collection of factual data pertaining to health and disease in a human population within a given geographic area.Parity: The number of offspring a female has borne. It is contrasted with GRAVIDITY, which refers to the number of pregnancies, regardless of outcome.Mass Screening: Organized periodic procedures performed on large groups of people for the purpose of detecting disease.Alleles: Variant forms of the same gene, occupying the same locus on homologous CHROMOSOMES, and governing the variants in production of the same gene product.Birth Intervals: The lengths of intervals between births to women in the population.Netherlands: Country located in EUROPE. It is bordered by the NORTH SEA, BELGIUM, and GERMANY. Constituent areas are Aruba, Curacao, Sint Maarten, formerly included in the NETHERLANDS ANTILLES.Neoplasm Staging: Methods which attempt to express in replicable terms the extent of the neoplasm in the patient.Hospital Mortality: A vital statistic measuring or recording the rate of death from any cause in hospitalized populations.Coronary Disease: An imbalance between myocardial functional requirements and the capacity of the CORONARY VESSELS to supply sufficient blood flow. It is a form of MYOCARDIAL ISCHEMIA (insufficient blood supply to the heart muscle) caused by a decreased capacity of the coronary vessels.EnglandDisease-Free Survival: Period after successful treatment in which there is no appearance of the symptoms or effects of the disease.Cross-Over Studies: Studies comparing two or more treatments or interventions in which the subjects or patients, upon completion of the course of one treatment, are switched to another. In the case of two treatments, A and B, half the subjects are randomly allocated to receive these in the order A, B and half to receive them in the order B, A. A criticism of this design is that effects of the first treatment may carry over into the period when the second is given. (Last, A Dictionary of Epidemiology, 2d ed)African Continental Ancestry Group: Individuals whose ancestral origins are in the continent of Africa.Treatment Failure: A measure of the quality of health care by assessment of unsuccessful results of management and procedures used in combating disease, in individual cases or series.Hypertension: 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.Chronic Disease: Diseases which have one or more of the following characteristics: they are permanent, leave residual disability, are caused by nonreversible pathological alteration, require special training of the patient for rehabilitation, or may be expected to require a long period of supervision, observation, or care. (Dictionary of Health Services Management, 2d ed)Occupational Diseases: Diseases caused by factors involved in one's employment.Gene Frequency: The proportion of one particular in the total of all ALLELES for one genetic locus in a breeding POPULATION.Diet: Regular course of eating and drinking adopted by a person or animal.Antineoplastic Combined Chemotherapy Protocols: The use of two or more chemicals simultaneously or sequentially in the drug therapy of neoplasms. The drugs need not be in the same dosage form.Reference Values: The range or frequency distribution of a measurement in a population (of organisms, organs or things) that has not been selected for the presence of disease or abnormality.WashingtonObesity: 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).Acute Disease: Disease having a short and relatively severe course.Models, Statistical: 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.Prostatic Secretory Proteins: Proteins secreted by the prostate gland. The major secretory proteins from the human prostate gland include PROSTATE-SPECIFIC ANTIGEN, prostate-specific acid phosphatase, prostate-specific membrane antigen, and prostate-specific protein-94.Ethnic Groups: A group of people with a common cultural heritage that sets them apart from others in a variety of social relationships.TaiwanDatabases, Factual: Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.GermanyPostoperative Complications: Pathologic processes that affect patients after a surgical procedure. They may or may not be related to the disease for which the surgery was done, and they may or may not be direct results of the surgery.Postmenopause: The physiological period following the MENOPAUSE, the permanent cessation of the menstrual life.Single-Blind Method: A method in which either the observer(s) or the subject(s) is kept ignorant of the group to which the subjects are assigned.Pregnancy Rate: The ratio of the number of conceptions (CONCEPTION) including LIVE BIRTH; STILLBIRTH; and fetal losses, to the mean number of females of reproductive age in a population during a set time period.Administration, Oral: The giving of drugs, chemicals, or other substances by mouth.Poisson Distribution: A distribution function used to describe the occurrence of rare events or to describe the sampling distribution of isolated counts in a continuum of time or space.Haplotypes: The genetic constitution of individuals with respect to one member of a pair of allelic genes, or sets of genes that are closely linked and tend to be inherited together such as those of the MAJOR HISTOCOMPATIBILITY COMPLEX.France: 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.Heart Atria: The chambers of the heart, to which the BLOOD returns from the circulation.EuropeOutcome Assessment (Health Care): Research aimed at assessing the quality and effectiveness of health care as measured by the attainment of a specified end result or outcome. Measures include parameters such as improved health, lowered morbidity or mortality, and improvement of abnormal states (such as elevated blood pressure).NorwaySpain: Parliamentary democracy located between France on the northeast and Portugual on the west and bordered by the Atlantic Ocean and the Mediterranean Sea.Dose-Response Relationship, Drug: The relationship between the dose of an administered drug and the response of the organism to the drug.Diabetes Mellitus: A heterogeneous group of disorders characterized by HYPERGLYCEMIA and GLUCOSE INTOLERANCE.Educational Status: Educational attainment or level of education of individuals.Statistics, Nonparametric: A class of statistical methods applicable to a large set of probability distributions used to test for correlation, location, independence, etc. In most nonparametric statistical tests, the original scores or observations are replaced by another variable containing less information. An important class of nonparametric tests employs the ordinal properties of the data. Another class of tests uses information about whether an observation is above or below some fixed value such as the median, and a third class is based on the frequency of the occurrence of runs in the data. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed, p1284; Corsini, Concise Encyclopedia of Psychology, 1987, p764-5)Blood Pressure: PRESSURE of the BLOOD on the ARTERIES and other BLOOD VESSELS.Canada: The largest country in North America, comprising 10 provinces and three territories. Its capital is Ottawa.Publication ComponentsBirth Weight: The mass or quantity of heaviness of an individual at BIRTH. It is expressed by units of pounds or kilograms.Area Under Curve: A statistical means of summarizing information from a series of measurements on one individual. It is frequently used in clinical pharmacology where the AUC from serum levels can be interpreted as the total uptake of whatever has been administered. As a plot of the concentration of a drug against time, after a single dose of medicine, producing a standard shape curve, it is a means of comparing the bioavailability of the same drug made by different companies. (From Winslade, Dictionary of Clinical Research, 1992)Coronary Artery Disease: Pathological processes of CORONARY ARTERIES that may derive from a congenital abnormality, atherosclerotic, or non-atherosclerotic cause.Gestational Age: The age of the conceptus, beginning from the time of FERTILIZATION. In clinical obstetrics, the gestational age is often estimated as the time from the last day of the last MENSTRUATION which is about 2 weeks before OVULATION and fertilization.Aspirin: The prototypical analgesic used in the treatment of mild to moderate pain. It has anti-inflammatory and antipyretic properties and acts as an inhibitor of cyclooxygenase which results in the inhibition of the biosynthesis of prostaglandins. Aspirin also inhibits platelet aggregation and is used in the prevention of arterial and venous thrombosis. (From Martindale, The Extra Pharmacopoeia, 30th ed, p5)Emergency Service, Hospital: Hospital department responsible for the administration and provision of immediate medical or surgical care to the emergency patient.Patient Compliance: Voluntary cooperation of the patient in following a prescribed regimen.Adenocarcinoma: A malignant epithelial tumor with a glandular organization.Residence Characteristics: Elements of residence that characterize a population. They are applicable in determining need for and utilization of health services.Bias (Epidemiology): Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions.Life Style: Typical way of life or manner of living characteristic of an individual or group. (From APA, Thesaurus of Psychological Index Terms, 8th ed)Urban Population: The inhabitants of a city or town, including metropolitan areas and suburban areas.Polymerase Chain Reaction: In vitro method for producing large amounts of specific DNA or RNA fragments of defined length and sequence from small amounts of short oligonucleotide flanking sequences (primers). The essential steps include thermal denaturation of the double-stranded target molecules, annealing of the primers to their complementary sequences, and extension of the annealed primers by enzymatic synthesis with DNA polymerase. The reaction is efficient, specific, and extremely sensitive. Uses for the reaction include disease diagnosis, detection of difficult-to-isolate pathogens, mutation analysis, genetic testing, DNA sequencing, and analyzing evolutionary relationships.Blood Grouping and Crossmatching: Testing erythrocytes to determine presence or absence of blood-group antigens, testing of serum to determine the presence or absence of antibodies to these antigens, and selecting biocompatible blood by crossmatching samples from the donor against samples from the recipient. Crossmatching is performed prior to transfusion.Data Interpretation, Statistical: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.Anti-Bacterial Agents: Substances that reduce the growth or reproduction of BACTERIA.North CarolinaMinnesotaAnti-Inflammatory Agents, Non-Steroidal: Anti-inflammatory agents that are non-steroidal in nature. In addition to anti-inflammatory actions, they have analgesic, antipyretic, and platelet-inhibitory actions.They act by blocking the synthesis of prostaglandins by inhibiting cyclooxygenase, which converts arachidonic acid to cyclic endoperoxides, precursors of prostaglandins. Inhibition of prostaglandin synthesis accounts for their analgesic, antipyretic, and platelet-inhibitory actions; other mechanisms may contribute to their anti-inflammatory effects.Length of Stay: The period of confinement of a patient to a hospital or other health facility.Pregnancy Complications: Conditions or pathological processes associated with pregnancy. They can occur during or after pregnancy, and range from minor discomforts to serious diseases that require medical interventions. They include diseases in pregnant females, and pregnancies in females with diseases.Combined Modality Therapy: The treatment of a disease or condition by several different means simultaneously or sequentially. Chemoimmunotherapy, RADIOIMMUNOTHERAPY, chemoradiotherapy, cryochemotherapy, and SALVAGE THERAPY are seen most frequently, but their combinations with each other and surgery are also used.Asthma: A form of bronchial disorder with three distinct components: airway hyper-responsiveness (RESPIRATORY HYPERSENSITIVITY), airway INFLAMMATION, and intermittent AIRWAY OBSTRUCTION. It is characterized by spasmodic contraction of airway smooth muscle, WHEEZING, and dyspnea (DYSPNEA, PAROXYSMAL).Ontario: A province of Canada lying between the provinces of Manitoba and Quebec. Its capital is Toronto. It takes its name from Lake Ontario which is said to represent the Iroquois oniatariio, beautiful lake. (From Webster's New Geographical Dictionary, 1988, p892 & Room, Brewer's Dictionary of Names, 1992, p391)Neoplasm Recurrence, Local: The local recurrence of a neoplasm following treatment. It arises from microscopic cells of the original neoplasm that have escaped therapeutic intervention and later become clinically visible at the original site.Health Status: The level of health of the individual, group, or population as subjectively assessed by the individual or by more objective measures.MassachusettsStatistics as Topic: 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.Social Class: A stratum of people with similar position and prestige; includes social stratification. Social class is measured by criteria such as education, occupation, and income.Rural Population: The inhabitants of rural areas or of small towns classified as rural.Hispanic Americans: Persons living in the United States of Mexican (MEXICAN AMERICANS), Puerto Rican, Cuban, Central or South American, or other Spanish culture or origin. The concept does not include Brazilian Americans or Portuguese Americans.Age of Onset: The age, developmental stage, or period of life at which a disease or the initial symptoms or manifestations of a disease appear in an individual.Pinacidil: A guanidine that opens POTASSIUM CHANNELS producing direct peripheral vasodilatation of the ARTERIOLES. It reduces BLOOD PRESSURE and peripheral resistance and produces fluid retention. (Martindale The Extra Pharmacopoeia, 31st ed)New YorkContinental Population Groups: Groups of individuals whose putative ancestry is from native continental populations based on similarities in physical appearance.Maternal Age: The age of the mother in PREGNANCY.Infusions, Intravenous: The long-term (minutes to hours) administration of a fluid into the vein through venipuncture, either by letting the fluid flow by gravity or by pumping it.Observer Variation: The failure by the observer to measure or identify a phenomenon accurately, which results in an error. Sources for this may be due to the observer's missing an abnormality, or to faulty technique resulting in incorrect test measurement, or to misinterpretation of the data. Two varieties are inter-observer variation (the amount observers vary from one another when reporting on the same material) and intra-observer variation (the amount one observer varies between observations when reporting more than once on the same material).Diabetes Mellitus, Type 2: A subclass of DIABETES MELLITUS that is not INSULIN-responsive or dependent (NIDDM). It is characterized initially by INSULIN RESISTANCE and HYPERINSULINEMIA; and eventually by GLUCOSE INTOLERANCE; HYPERGLYCEMIA; and overt diabetes. Type II diabetes mellitus is no longer considered a disease exclusively found in adults. Patients seldom develop KETOSIS but often exhibit OBESITY.Kidney Failure, Chronic: The end-stage of CHRONIC RENAL INSUFFICIENCY. It is characterized by the severe irreversible kidney damage (as measured by the level of PROTEINURIA) and the reduction in GLOMERULAR FILTRATION RATE to less than 15 ml per min (Kidney Foundation: Kidney Disease Outcome Quality Initiative, 2002). These patients generally require HEMODIALYSIS or KIDNEY TRANSPLANTATION.Causality: The relating of causes to the effects they produce. Causes are termed necessary when they must always precede an effect and sufficient when they initiate or produce an effect. Any of several factors may be associated with the potential disease causation or outcome, including predisposing factors, enabling factors, precipitating factors, reinforcing factors, and risk factors.Propensity Score: Conditional probability of exposure to a treatment given observed covariates.Australia: The smallest continent and an independent country, comprising six states and two territories. Its capital is Canberra.Epidemiologic Studies: Studies designed to examine associations, commonly, hypothesized causal relations. They are usually concerned with identifying or measuring the effects of risk factors or exposures. The common types of analytic study are CASE-CONTROL STUDIES; COHORT STUDIES; and CROSS-SECTIONAL STUDIES.Hemorrhage: Bleeding or escape of blood from a vessel.Isatis: A plant genus of the family BRASSICACEAE that is an ingredient of the preparation PC-SPES that is used to treat PROSTATIC HYPERPLASIA.Atrial Fibrillation: Abnormal cardiac rhythm that is characterized by rapid, uncoordinated firing of electrical impulses in the upper chambers of the heart (HEART ATRIA). In such case, blood cannot be effectively pumped into the lower chambers of the heart (HEART VENTRICLES). It is caused by abnormal impulse generation.Algorithms: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.Exercise: Physical activity which is usually regular and done with the intention of improving or maintaining PHYSICAL FITNESS or HEALTH. Contrast with PHYSICAL EXERTION which is concerned largely with the physiologic and metabolic response to energy expenditure.SEER Program: A cancer registry mandated under the National Cancer Act of 1971 to operate and maintain a population-based cancer reporting system, reporting periodically estimates of cancer incidence and mortality in the United States. The Surveillance, Epidemiology, and End Results (SEER) Program is a continuing project of the National Cancer Institute of the National Institutes of Health. Among its goals, in addition to assembling and reporting cancer statistics, are the monitoring of annual cancer incident trends and the promoting of studies designed to identify factors amenable to cancer control interventions. (From National Cancer Institute, NIH Publication No. 91-3074, October 1990)Urban Health: The status of health in urban populations.Wounds and Injuries: Damage inflicted on the body as the direct or indirect result of an external force, with or without disruption of structural continuity.Diabetes Complications: Conditions or pathological processes associated with the disease of diabetes mellitus. Due to the impaired control of BLOOD GLUCOSE level in diabetic patients, pathological processes develop in numerous tissues and organs including the EYE, the KIDNEY, the BLOOD VESSELS, and the NERVE TISSUE.Placebos: Any dummy medication or treatment. Although placebos originally were medicinal preparations having no specific pharmacological activity against a targeted condition, the concept has been extended to include treatments or procedures, especially those administered to control groups in clinical trials in order to provide baseline measurements for the experimental protocol.Carcinoma, Squamous Cell: A carcinoma derived from stratified SQUAMOUS EPITHELIAL CELLS. It may also occur in sites where glandular or columnar epithelium is normally present. (From Stedman, 25th ed)Infant, Low Birth Weight: An infant having a birth weight of 2500 gm. (5.5 lb.) or less but INFANT, VERY LOW BIRTH WEIGHT is available for infants having a birth weight of 1500 grams (3.3 lb.) or less.Contraceptives, Oral: Compounds, usually hormonal, taken orally in order to block ovulation and prevent the occurrence of pregnancy. The hormones are generally estrogen or progesterone or both.Heart Rate: The number of times the HEART VENTRICLES contract per unit of time, usually per minute.Tumor Markers, Biological: Molecular products metabolized and secreted by neoplastic tissue and characterized biochemically in cells or body fluids. They are indicators of tumor stage and grade as well as useful for monitoring responses to treatment and predicting recurrence. Many chemical groups are represented including hormones, antigens, amino and nucleic acids, enzymes, polyamines, and specific cell membrane proteins and lipids.Research Design: A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly.Seasons: Divisions of the year according to some regularly recurrent phenomena usually astronomical or climatic. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)New Zealand: A group of islands in the southwest Pacific. Its capital is Wellington. It was discovered by the Dutch explorer Abel Tasman in 1642 and circumnavigated by Cook in 1769. Colonized in 1840 by the New Zealand Company, it became a British crown colony in 1840 until 1907 when colonial status was terminated. New Zealand is a partly anglicized form of the original Dutch name Nieuw Zeeland, new sea land, possibly with reference to the Dutch province of Zeeland. (From Webster's New Geographical Dictionary, 1988, p842 & Room, Brewer's Dictionary of Names, 1992, p378)Premature Birth: CHILDBIRTH before 37 weeks of PREGNANCY (259 days from the first day of the mother's last menstrual period, or 245 days after FERTILIZATION).Likelihood Functions: 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.Uterine Cervical Neoplasms: Tumors or cancer of the UTERINE CERVIX.

Body mass decrease after initial gain following smoking cessation. (1/7400)

BACKGROUND: Although smoking cessation is strongly associated with subsequent weight gain, it is not clear whether the initial gain in weight after smoking cessation remains over time. METHOD: Cross-sectional analyses were made, using data from periodic health examinations for workers, on the relationship between body mass index (BMI) and the length of smoking cessation. In addition, linear regression coefficients of BMI on the length of cessation were estimated according to alcohol intake and sport activity, to examine the modifying effect of these factors on the weight of former smokers. RESULTS: Means of BMI were 23.1 kg/m2, 23.3 kg/m2, 23.6 kg/m2 for light/medium smokers, heavy smokers and never smokers, respectively. Among former smokers who had smoked > or = 25 cigarettes a day, odds ratio (OR) of BMI >25 kg/m2 were 1.88 (95% confidence interval [CI] : 1.05-3.35), 1.32 (95% CI : 0.74-2.34), 0.66 (95% CI: 0.33-1.31) for those with 2-4 years, 5-7 years, and 8-10 years of smoking cessation, respectively. The corresponding OR among those who previously consumed <25 cigarettes a day were 1.06 (95% CI: 0.58-1.94), 1.00 (95% CI: 0.58-1.71), and 1.49 (95% CI: 0.95-2.32). CONCLUSIONS: The results suggest that although heavy smokers may experience large weight gain and weigh more than never smokers in the few years after smoking cessation, they thereafter lose weight to the never smoker level, while light and moderate smokers gain weight up to the never smoker level without any excess after smoking cessation.  (+info)

Relation between obesity and breast cancer in young women. (2/7400)

This study was conducted to assess the relation between body size and risk of breast cancer among young women. A case-control study was conducted among women aged 21-45 years living in three counties in Washington State. Cases were women born after 1944 with invasive or in situ breast cancer that was diagnosed between January 1, 1983, and April 30, 1990. Controls were selected using random digit dialing and were frequency-matched to cases on the basis of age and county of residence. Interviews took place between 1986 and 1992. Body size was evaluated using indices from several different time periods. After adjustment for confounders, a decreased risk of breast cancer was found for women in the highest quintile of body mass index (weight (kg)/height (m)2) as compared with the lowest quintile (for maximum lifetime body mass index, odds ratio = 0.69, 95% confidence interval (CI) 0.51-0.94). Age modified the relation between body size and risk of breast cancer. The odds ratio for women in the highest quintile of maximum body mass index who were aged 21-35 years was 0.29 (95% CI 0.16-0.55), as compared with an odds ratio of 1.5 for women aged 36-45 years (95% CI 0.9-2.5) (p for interaction = 0.003). This study supports prior research showing a decreased risk of breast cancer associated with increased body size among premenopausal or young women. More detailed analysis in this study found a strong effect that was limited to the youngest age group (< or = 35 years).  (+info)

Risk factors for injuries and other health problems sustained in a marathon. (3/7400)

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)

Statistical inference by confidence intervals: issues of interpretation and utilization. (4/7400)

This article examines the role of the confidence interval (CI) in statistical inference and its advantages over conventional hypothesis testing, particularly when data are applied in the context of clinical practice. A CI provides a range of population values with which a sample statistic is consistent at a given level of confidence (usually 95%). Conventional hypothesis testing serves to either reject or retain a null hypothesis. A CI, while also functioning as a hypothesis test, provides additional information on the variability of an observed sample statistic (ie, its precision) and on its probable relationship to the value of this statistic in the population from which the sample was drawn (ie, its accuracy). Thus, the CI focuses attention on the magnitude and the probability of a treatment or other effect. It thereby assists in determining the clinical usefulness and importance of, as well as the statistical significance of, findings. The CI is appropriate for both parametric and nonparametric analyses and for both individual studies and aggregated data in meta-analyses. It is recommended that, when inferential statistical analysis is performed, CIs should accompany point estimates and conventional hypothesis tests wherever possible.  (+info)

Where do people go for treatment of sexually transmitted diseases? (5/7400)

CONTEXT: Major public health resources are devoted to the prevention of sexually transmitted diseases (STDs) through public STD clinics. However, little is known about where people actually receive treatment for STDs. METHODS: As part of the National Health and Social Life Survey, household interviews were performed from February to September 1992 with 3,432 persons aged 18-59. Weighted population estimates and multinomial response methods were used to describe the prevalence of self-reported STDs and patterns of treatment utilization by persons who ever had a bacterial or viral STD. RESULTS: An estimated two million STDs were self-reported in the previous year, and 22 million 18-59-year-olds self-reported lifetime STDs. Bacterial STDs (gonorrhea, chlamydia, nongonococcal urethritis, pelvic inflammatory disease and syphilis) were more common than viral STDs (genital herpes, genital warts, hepatitis and HIV). Genital warts were the most commonly reported STD in the past year, while gonorrhea was the most common ever-reported STD. Almost half of all respondents who had ever had an STD had gone to a private practice for treatment (49%); in comparison, only 5% of respondents had sought treatment at an STD clinic. Respondents with a bacterial STD were seven times more likely to report going to an STD clinic than were respondents with a viral STD--except for chlamydia, which was more likely to be treated at family planning clinics. Men were significantly more likely than women to go to an STD clinic. Young, poor or black respondents were all more likely to use a family planning clinic for STD treatment than older, relatively wealthy or white respondents. Age, sexual history and geographic location did not predict particular types of treatment-seeking. CONCLUSIONS: The health care utilization patterns for STD treatment in the United States are complex. Specific disease diagnosis, gender, race and income status all affect where people will seek treatment. These factors need to be taken into account when STD prevention strategies are being developed.  (+info)

Condom use and HIV risk behaviors among U.S. adults: data from a national survey. (6/7400)

CONTEXT: How much condom use among U.S. adults varies by type of partner or by risk behavior is unclear. Knowledge of such differentials would aid in evaluating the progress being made toward goals for levels of condom use as part of the Healthy People 2000 initiative. METHODS: Data were analyzed from the 1996 National Household Survey of Drug Abuse, an annual household-based probability sample of the noninstitutionalized population aged 12 and older that measures the use of illicit drugs, alcohol and tobacco. The personal behaviors module included 25 questions covering sexual activity in the past year, frequency of condom use in the past year, circumstances of the last sexual encounter and HIV testing. RESULTS: Sixty-two percent of adults reported using a condom at last intercourse outside of an ongoing relationship, while only 19% reported using condoms when the most recent intercourse occurred within a steady relationship. Within ongoing relationships, condom use was highest among respondents who were younger, black, of lower income and from large metropolitan areas. Forty percent of unmarried adults used a condom at last sex, compared with the health objective of 50% for the year 2000. Forty percent of injecting drug users used condoms at last intercourse, compared with the 60% condom use objective for high-risk individuals. Significantly, persons at increased risk for HIV because of their sexual behavior or drug use were not more likely to use condoms than were persons not at increased risk; only 22% used condoms during last intercourse within an ongoing relationship. CONCLUSIONS: Substantial progress has been made toward national goals for increasing condom use. The rates of condom use by individuals at high risk of HIV need to be increased, however, particularly condom use with a steady partner.  (+info)

Computed radiography dual energy subtraction: performance evaluation when detecting low-contrast lung nodules in an anthropomorphic phantom. (7/7400)

A dedicated chest computed radiography (CR) system has an option of energy subtraction (ES) acquisition. Two imaging plates, rather than one, are separated by a copper filter to give a high-energy and low-energy image. This study compares the diagnostic accuracy of conventional computed radiography to that of ES obtained with two radiographic techniques. One soft tissue only image was obtained at the conventional CR technique (s = 254) and the second was obtained at twice the radiation exposure (s = 131) to reduce noise. An anthropomorphic phantom with superimposed low-contrast lung nodules was imaged 53 times for each radiographic technique. Fifteen images had no nodules; 38 images had a total of 90 nodules placed on the phantom. Three chest radiologists read the three sets of images in a receiver operating characteristic (ROC) study. Significant differences in Az were only found between (1) the higher exposure energy subtracted images and the conventional dose energy subtracted images (P = .095, 90% confidence), and (2) the conventional CR and the energy subtracted image obtained at the same technique (P = .024, 98% confidence). As a result of this study, energy subtracted images cannot be substituted for conventional CR images when detecting low-contrast nodules, even when twice the exposure is used to obtain them.  (+info)

Cancer mortality in agricultural regions of Minnesota. (8/7400)

Because of its unique geology, Minnesota can be divided into four agricultural regions: south-central region one (corn, soybeans); west-central region two (wheat, corn, soybeans); northwest region three (wheat, sugar beets, potatoes); and northeast region four (forested and urban in character). Cancer mortality (1980-1989) in agricultural regions one, two, and three was compared to region four. Using data compiled by the National Center for Health Statistics, cancer mortality was summarized by 5-year age groups, sex, race, and county. Age-standardized mortality rate ratios were calculated for white males and females for all ages combined, and for children aged 0-14. Increased mortality rate ratios and 95% confidence intervals (CIs) were observed for the following cancer sites: region one--lip (men), standardized rate ratio (SRR) = 2.70 (CI, 1.08-6.71); nasopharynx (women), SRR = 3.35 (CI, 1.20-9.31); region two--non-Hodgkin's lymphoma (women), SRR = 1.35 (CI, 1.09-1.66); and region three--prostate (men), SRR = 1.12 (CI, 1.00-1.26); thyroid (men), SRR = 2.95 (CI, 1.35-6.44); bone (men), SRR = 2.09 (CI, 1. 00-4.34); eye (women), SRR = 5.77 (CI, 1.90-17.50). Deficits of smoking-related cancers were noted. Excess cancers reported are consistent with earlier reports of agriculturally related cancers in the midwestern United States. However, reports on thyroid and bone cancer in association with agricultural pesticides are few in number. The highest use of fungicides occurs in region three. Ethylenebisdithiocarbamates, whose metabolite is a known cause of thyroid cancer in rats, are frequently applied. This report provides a rationale for evaluation of the carcinogenic potential of this suspect agent in humans.  (+info)

*Robust confidence intervals

In statistics a robust confidence interval is a robust modification of confidence intervals, meaning that one modifies the non- ... Before trusting the results of 100 objects weighed just three times each to have confidence intervals calculated from σ, it is ... The 200 extra weighings served only to detect and correct for operator error and did nothing to improve the confidence interval ... He could then use a bootstrap calculation to determine a confidence interval narrower than that calculated from σ, and so ...

*Confidence interval

A confidence interval for the parameter θ, with confidence level or confidence coefficient γ, is an interval with random ... confidence interval does not mean that 95% of the sample data lie within the interval. A confidence interval is not a ... Various interpretations of a confidence interval can be given (taking the 90% confidence interval as an example in the ... The explanation of a confidence interval can amount to something like: "The confidence interval represents values for the ...

*Binomial proportion confidence interval

In statistics, a binomial proportion confidence interval is a confidence interval for the probability of success calculated ... "Confidence Intervals". Boston University School of Public Health. Wallis, Sean A. (2013). "Binomial confidence intervals and ... the interval is (1 − 3/n,1). There are several research papers that compare these and other confidence intervals for the ... The Agresti-Coull interval is also another approximate binomial confidence interval. Given X {\displaystyle X} successes in n ...

*CDF-based nonparametric confidence interval

... based nonparametric confidence intervals are a general class of confidence intervals around statistical functionals of a ... Given a confidence envelope for the CDF of F {\displaystyle F} it is easy to derive a corresponding confidence interval for the ... CDF-based confidence intervals require a probabilistic bound on the CDF of the distribution from which the sample were ... The equally spaced confidence interval around the empirical CDF allows for different rates of violations across the support of ...

*Sam Weerahandi

Lee, J. C., and Lin, S. H. (2004). Generalized confidence intervals for the ratio of means of two normal populations. Journal ... Mathew, T. and Webb, D. W. (2005). Generalized p-values and confidence intervals for variance components: Applications to Army ... Bebu, I., and Mathew, T. (2009). Confidence intervals for limited moments and truncated moments in normal and lognormal models ... Weerahandi, S. (1993). Generalized Confidence Intervals. JASA, 88, 899-905. Wu, J.F., and Hamada, M.S. (2009). Experiments: ...

*Bootstrapping (statistics)

... then percentile confidence-intervals are often inappropriate. There are several methods for constructing confidence intervals ... If the bootstrap distribution of an estimator is symmetric, then percentile confidence-interval are often used; such intervals ... confidence-interval for the population median is (26, 28.5), which is close to the interval for (25.98, 28.46) for the smoothed ... As a result, confidence intervals on the basis of a Monte Carlo simulation of the bootstrap could be misleading. Athreya states ...

*Bradley Efron

"Bootstrap confidence intervals for a class of parametric problems." Biometrika. Efron, B. (1987). "Better bootstrap confidence ... intervals". Journal of the American Statistical Association Efron, B. (1990). "More efficient bootstrap computations". Journal ...

*OpenMx

Confidence intervals are estimated robustly. The program has parallel processing built-in via links to parallel environments in ...

*Charles R. Marshall

Confidence intervals on stratigraphic ranges. Paleobiology 16:1-10. Presentation of the Charles Schuchert Award of the ... Marshall's first prominent work was on using confidence intervals to better estimate the full stratigraphic range of a lineage ...

*Score (statistics)

... in the formulation of confidence intervals; in demonstrations of the Cramér-Rao inequality. The score function also plays an ...

*Estimation statistics

... confidence intervals are also 83% prediction intervals: one experiment's confidence interval has an 83% chance of capturing any ... Confidence intervals behave in a predictable way. By definition, 95% confidence intervals have a 95% chance of capturing the ... As such, knowing a single experiment's 95% confidence intervals gives the analyst a plausible range for the population mean, ... Gardner, M. J.; Altman, D. G. (1986-03-15). "Confidence intervals rather than P values: estimation rather than hypothesis ...

*Quantile regression

Kocherginsky, M.; He, X.; Mu, Y. (2005). "Practical Confidence Intervals for Regression Quantiles". Journal of Computational ...

*Generalized additive model

Wahba, G. (1983). "Bayesian Confidence Intervals for the Cross Validated Smoothing Spline". Journal of the Royal Statistical ... Marra, G.; Wood, S.N. (2012). "Coverage properties of confidence intervals for generalized additive model components". ... Nychka, D. (1988). "Bayesian confidence intervals for smoothing splines". Journal of the American Statistical Association. 83. ... which can be used to produce confidence/credible intervals for the smooth components, f j {\displaystyle f_{j}} . The Gaussian ...

*CLs method (particle physics)

... however it differs from standard confidence intervals in that the stated confidence level of the interval is not equal to its ... Suppose that q ( X ) {\displaystyle q(X)} is a test statistic from which the confidence interval is derived, and let p θ = P ( ... doi:10.1140/epjc/s10052-011-1554-0. Leon Jay Gleser (2002). "[Setting Confidence Intervals for Bounded Parameters]: Comment". ... Mark Mandelkern (2002). "Setting Confidence Intervals for Bounded Parameters". Statistical Science. 17 (2): 149-159. doi: ...

*Muscle atrophy

... confidence interval of 0.11-0.594 kilograms (0.24-1.31 lb). The studies included in the meta-analysis had durations of 2-12 ... confidence interval: 0.11, 0.594; Z value=2.85; P=0.004). There were no significant fat mass changes between intervention and ... confidence interval: -0.32, 0.159; Z value=0.66; P=0.511). CONCLUSION: Beta-hydroxy-beta-methylbutyrate supplementation ...

*Statistical significance

"Confidence Interval or P-Value?". doi:10.3238/arztebl.2009.0335. StatNews #73: Overlapping Confidence Intervals and Statistical ... "Conclusions about statistical significance are possible with the help of the confidence interval. If the confidence interval ... Confidence levels and confidence intervals were introduced by Neyman in 1937. Statistical significance plays a pivotal role in ... ISBN 0-471-82211-6. Cumming, Geoff (2012). Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta- ...

*Survival analysis

The hazard ratio HR = exp(coef) = 1.58, with a 95% confidence interval of 0.934 to 2.68. Because the confidence interval for HR ... Kaplan-Meier curve for aml with the confidence bounds. # By default, R includes the confidence interval. plot(aml.survfit, xlab ... Don't print the confidence interval. plot(aml.survfit, xlab = "Time (weeks)", ylab="Proportion surviving", conf.int=FALSE, main ... The summary output also gives upper and lower 95% confidence intervals for the hazard ratio: lower 95% bound = 1.15; upper 95% ...

*Enzalutamide

... confidence interval, 0.15-0.23). In addition, a phase II trial began in March 2011 comparing enzalutamide with bicalutamide in ... confidence interval, range of 0.59-0.83), and radiographic progression-free survival, with an 81% reduction in risk of ...

*Human mortality from H5N1

... confidence interval, 1.06-1.23), and direct contact with sick poultry (odds ratio, 1.73; 95% confidence interval, 1.58-1.89). ... confidence interval, 0.96-1.12), sick or dead poultry in the household but with no direct contact (odds ratio, 1.14; 95% ... the mortality rates for the resulting human adapted pandemic strain cannot be predicted with any confidence. The global case ...

*Group size measures

Confidence interval for mean group size; Median group size, the median of group sizes calculated over groups; Confidence ... 2008) discuss the statistical problems associated with group size measures (calculating confidence intervals, 2-sample tests, ... Confidence interval for mean crowding. Imagine a sample with 3 groups, where group sizes are 1, 2, and 6 individuals, ... interval for median group size. As Jarman (1974) pointed out, average individuals live in groups larger than average. Therefore ...

*Depression in childhood and adolescence

... confidence interval, 1.29-3.83). A dose-response relationship between the number of episodes of depression during adolescence, ...

*Vietnamese people in the United Kingdom

See the source for 95% confidence intervals. "Meeting the needs of Vietnamese adult learners". National Institute of Adult ...

*Suits index

"Confidence Intervals for the Suits Index" (PDF). National Tax Journal. March 2003. Retrieved 2007-05-16. Distributive impacts ...

*British Cypriots

See the source for 95% confidence intervals. Georgiou, Myria (2001). "Crossing the boundaries of the ethnic home: Media ...

*Ghanaians in the United Kingdom

See the source for 95% confidence intervals. "Ghanaian London". Bbc.co.uk. 27 May 2005. Retrieved 8 December 2017. Van Hear, ...

*List of Battlecross concert tours and performances

Again, the band earned praise from the European press for a performance 'so full of confidence and conviction with metal ... Intervals and Night Verses. Upon announcement of the tour, it was reported by the press and fans that the tour made 'no sense' ...
Downloadable (with restrictions)! Recently a growing body of research has studied inference in settings where parameters of interest are partially identified. In many cases the parameter is real-valued and the identification region is an interval whose lower and upper bounds may be estimated from sample data. For this case confidence intervals (CIs) have been proposed that cover the entire identification region with fixed probability. Here, we introduce a conceptually different type of confidence interval. Rather than cover the entire identification region with fixed probability, we propose CIs that asymptotically cover the true value of the parameter with this probability. However, the exact coverage probabilities of the simplest version of our new CIs do not converge to their nominal values uniformly across different values for the width of the identification region. To avoid the problems associated with this, we modify the proposed CI to ensure that its exact coverage probabilities do converge
Santer et al 2008 (including realclimates Gavin Schmidt) sharply criticized Douglass et al for failing to properly consider the effect of autocorrelation of regression residuals on trend confidence intervals, which they described as a methodological error. The need to properly account for autocorrelation in confidence interval estimation is a fairly long-standing theme at CA and…
When we estimate a parameter such as the relative risk, each possible value of that parameter is the expected value under some hypothesis, and each hypothesis has a P-value. 8,9 What we call "the"P-value is the P-value for the null hypothesis. Approximately, each P-value is the probability of obtaining an estimate at least as far from a specified value as the estimate we have obtained, if that specified value were the true value. It follows that no P-value, for the null hypothesis or any other, is the probability that the specified hypothesis is true. As an obvious example, the hypothesis corresponding to the point estimate has a (two-sided) P-value of 1.0. However, we do not treat our point estimates as absolutely certain to be true. Neither is the point estimate, in general, the most probable value.. For a given estimate, the 95% confidence interval is the set of all parameter values for which P ≥ 0.05. For the value at each limit of a 95% confidence interval, P = 0.05 (two-sided). Thus, if ...
A 95% confidence interval under Neyman-Pearson is defined as the interval upon which if we took many samples of size n from the population, 95% of the intervals formed around the sample means would contain the population mean.. In the circumstance where you have knowledge of the population variance, this interval will have the same range for each sample, assuming each sample is of size n.. However, in the circumstance where you dont have knowledge of the population variance, each sample of size n will use its sample standard deviation and therefore the interval range will vary across the samples as a result.. With this in mind, I am struggling to see the material benefit, as a part of a piece of analysis, to provide a confidence interval when the population variance isnt known. It feels as though I am presenting a metric which a) requires the reader to consider an almost-abstract number of samples, b) has a range which is going to vary across those samples.. Are there any benefits to ...
A confidence interval is an indicator of your measurement's precision. It is also an indicator of how stable your estimate is, meaning that if you repeat your survey, your result will be close to your original estimate. Follow the steps below to calculate the confidence interval for your data.
Create plots involving sample size, half-width, estimated standard deviation, and confidence level for a confidence interval for the mean of a normal distribution or the difference between two means.
A random sample of 28 observations from a normally distributed population produced a mean x( bar over x = 24.2 and a standard deviation s =2.56. Find 95% confidence intervals for the population.
5 Answers to When the level of confidence and sample standard deviation remain the same, a confidence interval for a population mean based on a sample of n = 100... - 149304
dialysis Odds Ratio Std. Err. z P|z [95% Conf. Interval] logintercept .0001105 .0003682 -2.73 0.006 1.61e-07 .0756841 Dear ...
Is there anyone here who is fluent in the program MINITAB? If so, I how do I find 95% confidence limits for the mean response mu Y|x for each input
c) A further study is undertaken, in which 150 sample bottles are used. The sample mean and standard deviation are found to be x = 998 and s = 4 respectively ...
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m ,- binaryPGLMM(Y ~ X1, phy=phy, data=sim.dat) # from ?binaryPGLMM vcov.binaryPGLMM ,- function(object, ...) { object$B.cov } coef.binaryPGLMM ,- function(object, ...) { object$B[, 1] } confint(m ...
思路:原来以为很简单,结果发现会出现同一个点原地开始原地结束的情况。先遍历查询,然后把所有的坐标装入map中,value的值代表对应的属性,然后读取。 /** * Definition for an interval. * public class Interval { * int start; * int end; * Interval() { start =
A Sagittarian tends to look at the brighter side of things, and has a good dose of confidence. They act mostly driven by her desire, and are not
Downloadable! Experiments in psychology, where subjects estimate confidence intervals to a series of factual questions, have shown that individuals report far too narrow intervals. This has been interpreted as evidence of overconfidence in the preciseness of knowledge, a potentially serious violation of the rationality assumption in economics. Following these results a growing literature in economics has incorporated overconfidence in models of, for instance, financial markets. In this paper we investigate the robustness of results from confidence interval estimation tasks with respect to a number of manipulations: frequency assessments, peer frequency assessments, iteration, and monetary incentives. Our results suggest that a large share of the overconfidence in interval estimation tasks is an artifact of the response format. Using frequencies and monetary incentives reduces the measured overconfidence in the confidence interval method by about 65%. The results are consistent with the notion that
This article deals with the dependency(ies) of noninferiority test(s) when the two confidence interval method is employed. There are two different definitions of the two confidence interval method. One of the objectives of this article is to sort out some of the confusion in these two different definitions. In the first definition the two confidence interval method is considered as the fixed margin method that treats a noninferiority margin as a fixed constant after it is determined based on historical data. In this article the method is called the two confidence interval method with fixed margin. The issue of the dependency(ies) of noninferiority test(s) does not occur in this case. In the second definition the two confidence interval method incorporates the uncertainty associated with the estimation for the noninferiority margin. In this article the method is called the two confidence interval method with random margin. The dependency(ies) occurs, because the two confidence interval method(s) with
Abstract: In this study, we calculate confidence intervals for the mean of a normal data and a contaminated normal data. Some robust estimators against outliers are also considered to construct confidence intervals that are more resistant to outliers than the Student t confidence interval. The confidence intervals of these estimators are computed and compared with each other for normal and contaminated normal data to determine which is better. The performance of these confidence intervals is evaluated and compared by calculating the estimated coverage probability, the average width and the standard error by using simulation. Sps t followed by MAD t are recommended at any rate of contamination, while Student t is not preferred at all for contaminated data and the sample mean and the sample standard deviation are not good choices for constructing confidence interval, but highly recommended for normal data without outliers as expected ...
Computing a likelihood-ratio-based confidence interval is an iterative process. This process must be performed twice for each parameter, so the computational cost is considerable. Using a modified form of the algorithm recommended by Venzon and Moolgavkar (1988), you can determine that the cost of each endpoint computation is approximately the cost of estimating the original system. To request confidence intervals on estimated parameters, specify the PRL= option in the FIT statement. By default, the PRL option produces 95% likelihood ratio confidence limits. The coverage of the confidence interval is controlled by the ALPHA= option in the FIT statement. The following is an example of the use of the confidence interval options. ...
RECOMMENDED: If you have Windows errors then we strongly recommend that you download and run this (Windows) Repair Tool.. So, this process is centered, the distance to the nearer specification is DNS = [17.5-10.084] = 7.42 units, and the capability indexes are: The global standard. we.. Generally, I have the following data: CampaingOne 49% CampaingTwo 41% I need to use the three-sigma rule and check if the second value is in the interval [- 2 sigma.. Sources of error - epidemiolog - 10. Sources of error A systematic framework for identifying potential sources and impact of distortion in observational studies, with approaches to maintaining validity. . to compute a confidence interval for the mean. find the t values to use in confidence intervals. compute an estimate of the standard error.. Revisions with public records data are standard. represents a 95-percent.. VaR = [Expected Weighted Return of the Portfolio - (z-score of the confidence interval * standard deviation of the portfolio. return ...
Confidence intervals for densities built on the basis of standard nonparametric theory are doomed to have poor coverage rates due to bias. Studies on coverage improvement exist, but reasonably behaved interval estimators are needed. We explore the use of small bias kernel--based methods to construct confidence intervals, in particular using a geometric density estimator that seems particularly suited for this purpose ...
Figure 1. You estimate the population mean, by using a sample mean, plus or minus a margin of error. This is the 99.73% confidence interval, and the chance of this interval excluding the population mean is 1 in 370. Calculate Confidence Interval Variance Recall that with a normal distribution, 95% of the distribution is within 1.96 standard deviations of the mean. Standard error of a proportion or a percentage Just as we can calculate a standard error associated with a mean so we can also calculate a standard error associated with a For example, a series of samples of the body temperature of healthy people would show very little variation from one to another, but the variation between samples of the systolic blood The lower end of the CI is minus the margin of error, whereas the upper end of the CI is plus the margin of error. news While it will probably take time to appreciate and use confidence intervals, let me assure you its worth the pain. SE for a proprotion(p) = sqrt [(p (1 - p)) / n] ...
... a random sample of 250 observations taken from a Normal population. Sigma is known to be 14. If we were to construct another 95% confidence interval, this time using 1,000 observations, how would the length of this interval compare to the length of the original interval? ...
1. A study of 35 golfers showed that their average score on a particular course was 92. The standard deviation of the population is 5. A. Find the best point estimate of the mean. B. Find the 95% confidence interval of the mean score for all golfers. C. Find the 95% confidence interval of the mean score if a sample of 60 golf ...
first of all thank you for developing this nice tool. Ive recently used your package to plot Kaplan-Meier curves with 95% confidence interval for a study and the manuscript has been accepted in a medical journal. However, the journal recreates in-house all the figures and an .eps file was requested. When I export the picture as vector file I lost 95% confidence interval. I tried to bypass the issue by saving the figure in a raster format and converting to .eps but that was not acceptable for the journal because they cannot use each element in Adobe Illustrator ...
John says:. March 15, 2014 at 2:50 pm. I agree Andrew. I never teach my students to associate the word confident with the interval and try to describe it as just a way to label the interval. It could be the "orange" interval but the label were using is descriptive of the method.. However, if you genuinely are not in a situation where you can have any further certainty about whether the interval does, or does not contain the true value, then you can know the method you used makes you correct about the interval containing the mean 95% of the time. Some might call that 95% confidence. From a Bayesian perspective you might argue thats a rare occurrence, or that it never occurs. But thats a separate philosophical debate. I think that your average undergrad doing a project where they estimate an interval on a fairly large effect probably has pretty good standing to claim 95% confidence, whereas a scientist who estimates an interval containing 0 where there are sound reasons it should not be in the ...
Compute the half-width of a confidence interval for a binomial proportion or the difference between two proportions, given the sample size(s), estimated proportion(s), and confidence level.
A range of values, calculated from the sample observations, that are believed, with a particular probability, to contain the true parameter values. A 95% confidence interval, for example, implies that were the estimation process repeated again and again, then 95% of the calculated intervals could be expected to contain the true parameter value. Note that the stated probability level refers to properties of the interval and not to the parameter itself which is not considered a random variable. [1] ...
This paper presents a method to determine the confidence intervals of many simulation performance measures based on a single simulation. The confidence
Calculates the confidence interval of the best power fit for an input data set. This product does not support FPGA devices In the following illustration, the region between the upper and lower confidence bounds is the confidence interval. If the noise of y is Gaussian-distributed, you must fit the o
where tα/2;N = invStudentQ (α/2, N-1).. For α=0.05 and N=20 we get z0.025=1.96 and t0.025;20=2.093. This shows that for a fixed value of the standard deviation the confidence interval will always be wider if we had to estimate the standard deviations value from the data instead of its value being known beforehand.. ...
I challenge you to set aside time for yourself and truly see where you are finding your confidence and value. Its totally okay to find supplements to our confidence growth from compliments, accomplishments, and outside live… but our unshakeable confidence has to come from the truth of how we were created and how deeply we are loved. Its hard to question your value when you realize how much has already been done for you through our Father. Journal about your thoughts, spend intentional quiet time, and take one step closer to the unshakeable confidence you were created to have ...
(KudoZ) English to German translation of prescribing confidence: Vertrauen in das Rezept [Impfserum - Medical: Pharmaceuticals (Medical)].
Mayo and Gray introduced the leverage residual-weighted elemental (LRWE) classification of regression estimators and a new method of estimation called trimmed elemental estimation (TEE), showing the efficiency and robustness of TEE point estimates. Using bootstrap methods, properties of various trimmed elemental estimator interval estimates to allow for inference are examined and estimates with ordinary least squares (OLS) and least sum of absolute values (LAV) are compared. Confidence intervals and coverage probabilities for the estimators using a variety of error distributions, sample sizes, and number of parameters are examined. To reduce computational intensity, randomly selecting elemental subsets to calculate the parameter estimates were investigated. For the distributions considered, randomly selecting 50% of the elemental regressions led to highly accurate estimates.
What is the interpretation of a confidence interval following estimation of a Box-Cox transformation parameter ?? Several authors have argued that confidence intervals for linear model parameters ? can be constructed as if ? were known in advance, rather than estimated, provided the estimand is interpreted conditionally given ??. If the estimand is defined as ? (??), a function of the estimated transformation, can the nominal confidence level be regarded as a conditional coverage probability given ??, where the interval is random and the estimand is fixed? Or should it be regarded as an unconditional probability, where both the interval and the estimand are random? This article investigates these questions via large-n approximations, small-? approximations, and simulations. It is shown that, when model assumptions are satisfied and n is large, the nominal confidence level closely approximates the conditional coverage probability. When n is small, this conditional approximation is still good for
The Chi-Square distribution is used in the chi-square tests for goodness of fit of an observed distribution to a theoretical one and the independence of two criteria of classification of qualitative data. It is also used in confidence interval estimation for a population standard deviation of a normal distribution from a sample standard deviation. The Chi-Square distribution is a special case of the Gamma distribution [link to gamma]. PDF ...
Concepts of estimation and test of hypothesis, sampling distributions, confidence interval estimation and test of hypothesis for proportion(s), mean(s) and standard deviation(s), association and trend analysis, elementary experimental designs and analysis of variance. Note: Credit can be obtained for only one of STAT 2793, BA 2606 , PSYC 3913 . ...
Download a PDF file with page numbers here or view the table of contents below. Introduction Statistics in practice Learning statistics Foundations Identifying and summarizing data Population distributions Selecting individuals at random-probability Random sampling Central limit theorem-normal version Central limit theorem-t version Interval estimation Hypothesis testing The rejection region method The p-value method Hypothesis test errors Random…
Describe basic concept of simple regression (i.e.: the method of least squares, inferences about parameters from regression lines, interval estimation for linear regression, and assessing the goodness of fit of regression lines ...
It is possible to put upper confidence bounds on event risks when no events are observed, which may be useful when trying to ascertain possible risks for serious adverse events. A simple rule termed the rule of threes has been proposed such that if no events are observed in a group, then the upper confidence interval limit for the number of events is three, and for the risk (in a sample of size N) is 3/N (Hanley 1983). The application of this rule has not directly been proposed or evaluated for systematic reviews. However, when looking at the incidence of a rare event that is not observed in any of the intervention groups in a series of studies (which randomized trials, non-randomized comparison or case series), it seems reasonable to apply it, taking N as the sum of the sample sizes of the arms receiving intervention. However, it will not provide any information about the relative incidence of the event between two groups.. The value 3 coincides with the upper limit of a one-tailed 95% ...
This part of ASQ/ANSI/ISO 16269 specifies the procedures for establishing a point estimate and confidence intervals for the median of any continuous probability distribution of a population, based on a random sample size from the population. These procedures are distribution-free, i.e. they do not require knowledge of the family of distributions to which the population distribution belongs. Similar procedures can be applied to estimate quartiles and percentiles.. ...
View Notes - Week5&6_IntroInference from STAT 301 at Texas A&M. Week 5&6: Introduction to Inference Week 5&6: Introduction to Inference Confidence Intervals In statistics, when we cannot
The Centre for Confidence and Well-beings core activities include providing information, networking interested parties and improving the quality of confidence building approaches and activities through the provision of workshops and conferences and the dissemination or development of tools for evaluation.
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Manufacturers' confidence has seen its sharpest fall for 28 years during the 'exceptional economic turbulence' of the past three months, the CBI warned today.
The problem of estimation of sequestered parasites Plasmodium falciparum in malaria, based on measurements of circulating parasites, is addressed. It is assumed that all (death, transition, recruitment and infection) rates in the model of a patient are uncertain (just intervals of admissible values are given) and the measurements are subject to a bounded noise, then an interval observer is designed. Stability of the observer can be verified by a solution of LMI. The efficiency of the observer is demonstrated in simulation.
Fill in your details below or click an icon to log in: Email (required) (Address never made public) Name (required) Website You are commenting using your WordPress.com account. (LogOut/Change) You are But confidence intervals provide an essential understanding of how much faith we can have in our sample estimates, from any sample size, from 2 to 2 million. The standard error for the percentage of male patients with appendicitis is given by: In this case this is 0.0446 or 4.46%. Calculate 95 Confidence Interval Proportion This means that if we repeatedly compute the mean (M) from a sample, and create an interval ranging from M - 23.52 to M + 23.52, this interval will contain the Confidence Interval Calculator for a Completion Rate What five users can tell you that 5000 cannot How to Conduct a Usability test on a Mobile Device Nine misconceptions about statistics and For many biological variables, they define what is regarded as the normal (meaning standard or typical) range. A t table shows the ...
StaTool - Statistics and Probability Tools for Windows- Hypothesis testing - Confidence interval estimation - Probability distributions - One variable statistics - Two variables statistics - Total Probability Law and Bayes Theorem
The interaction index has been discussed by several authors, such as Berenbaum (9), Tallarida (14), and Meadows and colleagues (15). The definition of the interaction index coincides with Chou and Talalays definition of the combination index for mutually exclusive drugs, published in 1984 (16). The field of drug combination research spans more than 100 years and has been addressed within many disciplines. Chou and Talalays seminal article has made important contributions and has been widely cited in the literature. However, this article was not the first nor the only one that generates concepts supporting Eqs. 1 and 2 and applies these equations to study drug interactions. Lee and colleagues directed the reader to reviews of these methods, with proper citation of the appropriate references therein (17), and derived the confidence interval estimation for the interaction index and compared its performance with four methods based on response surface models. Although several equations are indeed ...
tions," John Wiley & Sons Inc., New York, 1981. [1] A. Agresti and B. A. Coull, "Approximate Is Better than [15] S. R. Lipsitz, K. B. G. Dear, N. M. Laird and G. Molen- Exact for Interval Estimation of Binomial Proportions," berghs, "Tests for Homogeneity of the Risk Difference American Statistical Association, Vol. 52, 1998, pp. 119- When Data Are Sparse," Biometrics, Vol. 54, No. 1, 1998, [2] A. Agresti and B. Caffo, "Simple and Effective Confi- [16] D. G. Kleinbaum, L. L. Kupper and H. Morgenstern, dence Intervals for Proportions and Differences of Pro- "Epidemiologic Research: Principles and Quantitative portions Result from Adding Two Successes and Two Methods," Lifetime Learning Publications, Belmont, 1982. Failures," The American Statistician, Vol. 54, No. 4, 2000, [17] D. B. Petitti, "Meta-Analysis, Decision Analysis and Cost-Effectiveness Analysis: Methods for Quantitative [3] B. K. Ghosh, "A Comparison of Some Approximate Con- Synthesis in Medicine," Oxford University Press, Oxford, ...
Atio; CI, Confidence Interval; AUC, area under the ROC curve. a Odds Ratio for any increase of one unit. { Eledoisin web p-value of the Wald statistic.
Find right answers right now! Which test statistic should u use when to get confidence interval given only number in a sample, sample mean & sample standard deviation? More questions about Science & Mathematics, which
Once again, we can now pull together several separate concepts and create a new statistical procedure that you can use, this time called the confidence interval. If we draw a single sample of participants from a population and compute the mean for that sample, we are essentially estimating the mean for the population. We would like to know how close our estimate of that population mean really is. There is, unfortunately, no way of knowing the answer to that question from just a single sample, but we can approach the question in a different way. Instead of simply saying that the sample mean is our best estimate of the population mean, we can give people an idea of how good an estimate it is by computing a confidence interval. A confidence interval is a range of scores in which we can predict that the mean falls a given percentage of the time. For example, a 95% confidence interval is a range in which we expect the population mean to fall 95% of the time. How wide or narrow that interval of scores ...
Video created by Johns Hopkins University for the course Statistical Reasoning for Public Health 1: Estimation, Inference, & Interpretation. Understanding sampling variability is the key to defining the uncertainty in any given sample/samples ...
This MATLAB function computes 95% confidence intervals for the estimated parameters from fitResults, an NLINResults object or OptimResults object returned by the sbiofit function.
is not specified), an exact p-value is computed if both samples contain less than 50 finite values and there are no ties. Otherwise, a normal approximation is used. Optionally, a nonparametric confidence interval and an estimator for s are computed. If exact p-values are available, an exact confidence interval is obtained by the algorithm described in Bauer (1972), and the Hodges-Lehmann estimator is employed. Otherwise, the returned confidence interval and point estimate are based on normal approximations. Note that mid-ranks are used in the case of ties rather than average scores as employed in Hollander & Wolfe (1973). See, e.g., Hajek, Sidak and Sen (1999), pages 131ff, for more information. ...
Click Here Confidence. Can you increase confidence? Could you be a the one person in the group, that confident person, other people want to be like? Increase Confidence Confidence is a state, an emotional state. You feel different emotional states throughout the day. At one point your happy,…
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Confidence rose in Europe to an index score of 79, its highest level in five years North American confidence remained optimistic at 101, but declined five points from the first...
The Centre for Confidence and Well-beings core activities include providing information, networking interested parties and improving the quality of confidence building approaches and activities through the provision of workshops and conferences and the dissemination or development of tools for evaluation.
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PREFACE xiii. 1. INTRODUCTION 1. 1.1 Regression and Model Building 1. 1.2 Data Collection 5. 1.3 Uses of Regression 9. 1.4 Role of the Computer 10. 2. SIMPLE LINEAR REGRESSION 12. 2.1 Simple Linear Regression Model 12. 2.2 Least-Squares Estimation of the Parameters 13. 2.3 Hypothesis Testing on the Slope and Intercept 22. 2.4 Interval Estimation in Simple Linear Regression 29. 2.5 Prediction of New Observations 33. 2.6 Coeffi cient of Determination 35. 2.7 A Service Industry Application of Regression 37. 2.8 Using SAS and R for Simple Linear Regression 39. 2.9 Some Considerations in the Use of Regression 42. 2.10 Regression Through the Origin 45. 2.11 Estimation by Maximum Likelihood 51. 2.12 Case Where the Regressor x is Random 52. 3. MULTIPLE LINEAR REGRESSION 67. 3.1 Multiple Regression Models 67. 3.2 Estimation of the Model Parameters 70. 3.3 Hypothesis Testing in Multiple Linear Regression 84. 3.4 Confidence Intervals in Multiple Regression 97. 3.5 Prediction of New Observations 104. 3.6 A ...
Being somewhat open minded, I decided to have a new look at Bayes, and therefore got "Doing Bayesian Data Analysis: A Tutorial with R and BUGS". Its been highly reviewed, and has all those cute doggies on the cover (not explained, either). The first half of the book is built on Bernoulli and binomial distribution; a lot of coin flipping. Chapter 11 gets to the heart of the matter, "Null Hypothesis Significance Testing" (NHST, for short). Those who embrace Bayes (nearly?) universally object to usual statistical testing and confidence interval estimation, because theyre based on testing whether values equal, as assumed. This is the Null Hypothesis: that two means are equal, for example. We assume that two samples (or one sample compared to a known control) have the same value for the mean, and set about to test whether the data support that equality. Depending on what the data say, we either accept or reject the null hypothesis. We dont get to say that the true mean (in this example) is the ...
This course introduces students to the logic and procedures of descriptive and inferential statistical analysis as they applied in social and natural sciences. It emphasizes quantitative reasoning skills involving assessment of data quality, original analysis, careful interpretation of claims and effective communication appropriate to reading and preparing both popular press and scientific reports. Topics include correlation/regression, ANOVA, and selected non parametric procedures, with statistical software used to support systematic attention to graphical exploration, power, effect size, and confidence interval estimation. Applications will cover multiple disciplines but will give added attention to examples from the natural sciences.. ...
Introduction to the theoretical concepts underlying quantitative methods in psychology. Topics include set theory, probability theory including the basic probability density functions and their cumulative distributions, joint events and stochastic independence, sampling theory and sampling distributions (including the binomial, normal, t, chi-square and F distributions), parameter estimation, interval estimation, the t-test, hypothesis testing, power, and some nonparametric statistics. PREREQ: Introductory Statistics and Graduate standing ...
Dorey, F. J. and Korn, E. L. (1987), Effective sample sizes for confidence intervals for survival probabilities. Statist. Med., 6: 679-687. doi: 10.1002/sim.4780060605 ...
People often speak and write about values of treatment effects outside their confidence intervals as being "excluded." For example; "the risk ratio for major morbidity was 0.98 (95% CI 0.91, 1.06), which excluded any clinically important effects." I just made that up but you often see and hear similar statements. What understanding do people take from it? There are two possible interpretations.. First, the straightforward meaning that clinically important values are outside the confidence interval. This is using "exclude" just as the opposite of "include" to make a statement about what is and isnt inside the confidence interval.. But there is another interpretation, or another layer of interpretation, which I suspect is very common, and results from the meaning of "exclude" as something a bit stronger. Dictionary definitions give things like "to keep out, reject or not consider, shut or keep out," which have a sense that excluding something is actively rejecting it. Using that word may ...
specifies the type of probability for the PROBWIDTH= option. A value of CONDITIONAL (the default) indicates the conditional probability that the confidence interval half-width is at most the value specified by the HALFWIDTH= option, given that the true mean is captured by the confidence interval. A value of UNCONDITIONAL indicates the unconditional probability that the confidence interval half-width is at most the value specified by the HALFWIDTH= option. You can use the alias GIVENVALIDITY for CONDITIONAL. The PROBTYPE= option can be used only with the CI=T analysis. For information about specifying the keyword-list, see the section Specifying Value Lists in Analysis Statements. ...
Evaluating Interval Estimates for Comparing Two Proportions with Rare Events - Bayesian probability interval;confidence interval;rare events;risk ratio;risk difference;
To lose confidence in ones body is to lose confidence in oneself.- Simone De BeauvoirSelf-image is inextricably linked to body image, especially for young females. In her book, Reviving Ophelia - Saving the Selves of Adolescent Girls, Pipher (1994) ...
Recently a student asked about the difference between confint() and confint.default() functions, both available in the MASS library to calculate confidence intervals from logistic regression models. The following example demonstrates that they yield d.... Read more » ...
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Approximate confidence intervals are given for the odds ratios derived from the covariates.. Bootstrap estimates A bootstrap procedure may be used to cross-validate confidence intervals calculated for odds ratios derived from fitted logistic models (Efron and Tibshirani, 1997; Gong, 1986). The bootstrap confidence intervals used here are the bias-corrected type.. The mechanism that StatsDirect uses is to draw a specified number of random samples (with replacement, i.e. some observations are drawn once only, others more than once and some not at all) from your data. These re-samples are fed back into the logistic regression and bootstrap estimates of confidence intervals for the model parameters are made by examining the model parameters calculated at each cycle of the process. The bias statistic shows how much each mean model parameter from the bootstrap distribution deviates from observed model parameters.. Classification and ROC curve The confidence interval given with the likelihood ...
Purposes and limitations of statistics; Theory, measurement, and mathematics; Univariate descriptive statistics; Nominal scales: proportions, percentages and ratios; Interval scales: frequency distributions and graphics presentation; Interval scales: measures of central tendency; Measures of dispersion; The normal distribution; Inductive statistics; Introduction to inductive statistics; Probability; Testing hypotheses: the binomial distribution; Single-sample tests involving means and proportions; Point and interval estimation; Bivariate and multivariate statistics; Two-sample tests: difference of means and proportions; Ordinal scales: two-sample nonparametric tests; Nominal scales: contigency problems; Analysis of variance; Correlation and regression; multiple and partial correlation; Analysis of covariance, dummy variables, and other applications of the linear model; Sampling; Appendix; Index.
The secret to putting well is confidence. Putting is the most important part of the game and you can only excel with confidence.. The good news is that putting isn’t difficult, you know you can make a short putt. The bad news is you know how difficult it is to consistently make short putts. Once you start to miss the short putts, your confidence wanes. To start holing them again you need a boost to your confidence, and there in lies the problem. How do you regain your confidence? How do you conquer the game of confidence as putting is often referred to?. Repeatedly missing short putts is no fun and destroys the enjoyment you should have playing this wonderful game. Do you feel humiliated at missing another short putts, it’s simply embarrassing. No wonder it can feel like you are on a slippery slope as your game slips into an exercise of hitting and hoping. Putting with doubt and without confidence is a card wrecker, but I bet it hasn’t always been that way!. Your current ...
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Hi, Is there any way to save the confidence limits in the Fit Y by X platform in JMP9? I could easily show them on the graph but cannot find out how
A confidence interval estimate of μ is a range of values used to estimate a population parameter (interval estimates are normally used more than point estimates because it is very unlikely that the sample mean would match exactly with the population mean). The interval estimate uses a margin of error about the point estimate. For example if you have a point estimate of 12.25 with a margin of error of 1.75, then the interval estimate would be (10.5 to 14). Before you find an interval estimate, you should first determine how confident you want to be that your interval estimate contains the population mean. ...
7. We have always encouraged you to give detailed answers on the getting to know us section. These particular expectant parents seemed to be more inclined to read those who gave short concise answers. So what should you do in the future? If youre detail oriented and want to give longer answers continue to do that. If youre a short answer type of person then give short answers. The lesson here is there is no hard and fast rule for any of this and the way you do things should be representative of who you are ...
Analysis for 500 uniform random numbers 1: Sample Size = 500 2: Location Mean = 0.50783 Standard Deviation of Mean = 0.013163 95% Confidence Interval for Mean = (0.48197,0.533692) Drift with respect to location? = NO 3: Variation Standard Deviation = 0.294326 95% Confidence Interval for SD = (0.277144,0.313796) Drift with respect to variation? (based on Levenes test on quarters of the data) = NO 4: Distribution Normal PPCC = 0.9771602 Normal Anderson-Darling = 5.7198390 Data are Normal? (as tested by Normal PPCC) = NO (as tested by Anderson-Darling) = NO Uniform PPCC = 0.9995683 Uniform Anderson-Darling = 0.9082221 Data are Uniform? (as tested by Uniform PPCC) = YES (as tested by Anderson-Darling) = YES 5: Randomness Autocorrelation = -0.03099 Data are Random? (as measured by autocorrelation) = YES 6: Statistical Control (i.e., no drift in location or scale, data is random, distribution is fixed, here we are testing only for fixed uniform) Data Set is in Statistical Control? = YES ...
Lower dairy returns and the high New Zealand dollar are starting to hit farmer confidence levels, according to the latest bi-monthly AC Nielsen/ Rabobank Rural Confidence Survey
Peter Flom : , Hello again , , Using ProText on a Windows machine , , I am having some problem with crossreferncing. , , I have , , The odds ratios and the confidence limits are , in table\ref{T:ordORest}. .MORE TEXT , , , , \begin{table}\label{T:ordORest} , \begin{tabular}{,l,r,r,r,}\hline , Effect & Point estimate & \multicolumn{2}{c,}{95\% confidence , limits}\\\hline , Factor 1 & 0.269 & 0.209 & 0.345 \\\hline , Age & 1.093 & 1.004 & 1.191 \\\hline , Female & 0.248 & 0.172 & 0.359 \\\hline , \end{tabular} , \caption{Odds ratios and confidence limits: Ordinal model} , \end{table} , , when I typeset this, using pdfLaTeX (or Texify, or LaTeX) I do not get , correct crossreferences. In pdfLaTeX and texify I get a thin red box. , In , Latex, I get a blank space. , , I have similar problems with other crossrefences to tables..... have you tried placing the \label AFTER the \caption? -- /daleif ``You cannot help men permanently by doing for them what they could and should do for themselves. -- ...
Thank you for your interest in spreading the word about The BMJ.. NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.. ...
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StocksHaven Investments news portal takes a closer look at the Consumer Confidence Index as of May 2009, which displays high optimism towards a faster than expected economic recovery., , , , Confidence am...
Learn about error and uncertainty in science. Includes information on how scientists identify and measure error and uncertainty, and how confidence is reported.
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Nevertheless, determined to get it right this time, I accessed the reference that was provided and dug in. After about five minutes on true and false positives, true and false negatives, "specificity", "sensitivity", "confidence intervals", and calculating the "number of patients needed to treat inorder to improve the outcome for one patient in a thirteen year period" my brain turned to marshmallow fluff. It was hopeless ...
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Submental fullness doesnt only occur in the aging adult. The double-chin is an age-old problem. Fortunately, aesthetic medicine has caught up with it.
Includes bibliographical references (p. 628-641) and index. PREFACE xiii 1. INTRODUCTION 1 1.1 Regression and Model Building 1 1.2 Data Collection 5 1.3 Uses of Regression 9 1.4 Role of the Computer 10 2. SIMPLE LINEAR REGRESSION 12 2.1 Simple Linear Regression Model 12 2.2 Least-Squares Estimation of the Parameters 13 2.3 Hypothesis Testing on the Slope and Intercept 22 2.4 Interval Estimation in Simple Linear Regression 29 2.5 Prediction of New Observations 33 2.6 Coeffi cient of Determination 35 2.7 A Service Industry Application of Regression 37 2.8 Using SAS and R for Simple Linear Regression 39 2.9 Some Considerations in the Use of Regression 42 2.10 Regression Through the Origin 45 2.11 Estimation by Maximum Likelihood 51 2.12 Case Where the Regressor x is Random 52 3. MULTIPLE LINEAR REGRESSION 67 3.1 Multiple Regression Models 67 3.2 Estimation of the Model Parameters 70 3.3 Hypothesis Testing in Multiple Linear Regression 84 3.4 Confidence Intervals in Multiple Regression 97 3.5 ...
A patient with cryptogenic fibrosing alveolitis, with both mural and desquamative features, had two lung biopsies at the times of coronary artery surgery. These lung specimens were studied, using light and electron microscopy, with immunofluorescence techniques and electron microanalysis. In addition to the typical changes of cryptogenic fibrosing alveolitis previously reported, we found "blue-staining bodies" within alveolar macrophages and giant cells. These bodies were 15--25 micrometer in diameter with an iron rich outer rim and core of connective tissue mucin--possibly chondroitin sulphate or dermatan sulphate. It seems unlikely that these "blue bodies" were due to fibreglass dust to which the patients had had a trivial exposure, but their exact nature and significance remains unclear.. ...
PubMed journal article Lung cancer and cryptogenic fibrosing alveolitis. A population-based cohort stud were found in PRIME PubMed. Download Prime PubMed App to iPhone or iPad.
Probability, conditional probability, random variables, Expected Value, Specific discrete and continuous distributions, e.g. binomial, Poisson, geometric, Pascal, hypergeometric, Uniform, exponential and normal, Poisson process, Multidimensional random variables, Multinomial and bivariate normal distributions, Moment generating function, Law of large numbers and central limit theorem, Sampling distributions, Point and interval estimation, Testing of hypothesis, Goodness of fit and contingency tables. Linear regression ...

Confidence interval - WikipediaConfidence interval - Wikipedia

Confidence interval for specific distributions[edit]. *Confidence interval for binomial distribution. *Confidence interval for ... A confidence interval for the parameter θ, with confidence level or confidence coefficient γ, is an interval with random ... Confidence region[edit]. Main article: Confidence region. Confidence regions generalize the confidence interval concept to deal ... An interactive introduction to Confidence Intervals. *Confidence Intervals: Confidence Level, Sample Size, and Margin of Error ...
more infohttps://en.wikipedia.org/wiki/Confidence_interval

Calculating confidence intervalsCalculating confidence intervals

... such interval estimates called confidence intervals Joe calculates the general formula for all confidence intervals is equal to ... An interval estimate provides more information about a population characteristic than does a point estimate and therefore ... provides a confidence level for the estimate. Joe Schmuller investigates ... such interval estimates called confidence intervals Joe calculates the general formula for all confidence intervals is equal to ...
more infohttps://www.lynda.com/Excel-tutorials/Calculating-confidence-intervals/422098/459861-4.html

Robust misinterpretation of confidence intervals | SpringerLinkRobust misinterpretation of confidence intervals | SpringerLink

Confidence intervals make a difference: Effects of showing confidence intervals on inferential reasoning. Educational and ... Jaynes, E. T. (1976). Confidence intervals vs Bayesian intervals. In W. L. Harper & C. A. Hooker (Eds.), Foundations of ... Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly ... Cumming, G., & Finch, S. (2001). A primer on the understanding, use, and calculation of confidence intervals that are based on ...
more infohttps://link.springer.com/article/10.3758%2Fs13423-013-0572-3

Confidence intervals for partially identified parametersConfidence intervals for partially identified parameters

... the identification region is an interval whose lower and upper bounds may be estimated from sample data. Confidence intervals ... and in large samples is comparable to the difference of one and twosided confidence intervals. A complication arises from the ... Here we introduce conceptually different interval estimates that asymptotically cover each element in the identification region ... Horowitz and Manski (1998, 2000) proposed and applied interval estimates that asymptotically cover the entire identification ...
more infohttps://ideas.repec.org/p/ifs/cemmap/09-03.html

Confidence Intervals for Partially Identified ParametersConfidence Intervals for Partially Identified Parameters

For this case confidence intervals (CIs) have been proposed that cover the entire identification region with fixed probability ... Here, we introduce a conceptually different type of confidence interval. Rather than cover the entire identification region ... In many cases the parameter is real-valued and the identification region is an interval whose lower and upper bounds may be ... "Confidence intervals for partially identified parameters," CeMMAP working papers CWP09/03, Centre for Microdata Methods and ...
more infohttps://ideas.repec.org/a/ecm/emetrp/v72y2004i6p1845-1857.html

Statistical significance and confidence intervals | The BMJStatistical significance and confidence intervals | The BMJ

provided and confidence intervals overlapping, it is not reasonable to. imply the means in the population are different. If the ... Statistical significance and confidence intervals. BMJ 2009; 339 doi: https://doi.org/10.1136/bmj.b3401 (Published 02 September ...
more infohttp://www.bmj.com/content/339/bmj.b3401/rapid-responses

Confidence IntervalsConfidence Intervals

... Hello.. Im having trouble with this question. I can do part (a) but i dont no where to start on ... b) Construct the 99% confidence interval for the true mean using a distribution appropriate for this small sample.. (c) A ... Using an appropriate distribution, calculate the 99% confidence interval for the true mean.. Any help would be appreciated, ...
more infohttp://mathhelpforum.com/statistics/90565-confidence-intervals-print.html

Confidence Intervals.Confidence Intervals.

... A random sample of 20 phials was drawn from a large consignment. The volume of the phails was known to ... Construct the 99% confidence limits for the mean volume of the phails in the consignment?. ...
more infohttp://mathhelpforum.com/new-users/207580-confidence-intervals-print.html

st: standardized confidence intervals using mlogitst: standardized confidence intervals using mlogit

... From. Staimez, Lisa Rachel ,[email protected],. To. [email protected] ... st: standardized confidence intervals using mlogit. Date. Mon, 19 Sep 2011 19:38:43 +0000. Dear Listers, I have been trying ... Can someone please point me to the right command or tell me how to produce the confidence intervals? Second, I am unable to ... RE: st: standardized confidence intervals using mlogit *From: Cameron McIntosh ,[email protected], ...
more infohttps://www.stata.com/statalist/archive/2011-09/msg00811.html

Confidence intervals for probability distribution parameters - MATLAB paramciConfidence intervals for probability distribution parameters - MATLAB paramci

... confidence interval for each parameter in probability distribution pd. ... Confidence interval. array. Confidence interval, returned as a p-by-2 array containing the lower and upper bounds of the 100(1- ... to compute the confidence intervals using the Wald method, or lr. to compute the confidence intervals using the likelihood ... For example, you can specify a different percentage for the confidence interval, or compute confidence intervals only for ...
more infohttps://www.mathworks.com/help/stats/prob.normaldistribution.paramci.html

RE: st: standardized confidence intervals using mlogitRE: st: standardized confidence intervals using mlogit

... From. Cameron McIntosh ,[email protected],. To. STATA LIST ,[email protected] ... RE: st: standardized confidence intervals using mlogit. Date. Mon, 19 Sep 2011 17:02:09 -0400. Lisa, Im not sure, but I think ... Can someone please point me to the right command or tell me how to produce the confidence intervals? , , Second, I am unable to ... st: standardized confidence intervals using mlogit *From: Staimez, Lisa Rachel ,[email protected], ...
more infohttps://www.stata.com/statalist/archive/2011-09/msg00813.html

Bootstrapping - Confidence Intervals, Bootstrapping, and Plotting | CourseraBootstrapping - Confidence Intervals, Bootstrapping, and Plotting | Coursera

This module covers Confidence Intervals, Bootstrapping, and Plotting. These are core concepts in mathematical biostatistics and ... These percentile confidence intervals. Theyre not very good.. You can improve on bootstrap confidence intervals by correcting ... Confidence Intervals, Bootstrapping, and Plotting. This module covers Confidence Intervals, Bootstrapping, and Plotting. These ... the intervals. And the bootstrap procedure, the one I. would recommend is this so called BCA, confidence interval, the ...
more infohttps://www.coursera.org/learn/biostatistics/lecture/ySRTV/bootstrapping

WikiGenes - Confidence IntervalsWikiGenes - Confidence Intervals

... confidence interval = 1.03-100) [38].. *RESULTS: Three (12%, exact 95% confidence interval [CI] = 2% to 30%) families carried ... 95 percent confidence interval, 1.5 to 7.6) [18].. *The 95 percent confidence intervals for the difference between the controls ... 95 percent confidence interval, 37 to 78 percent) and 94 percent (95 percent confidence interval, 86 to 100 percent), ... 95 percent confidence interval, 28 to 71) in the amphotericin B group and 64 days (95 percent confidence interval, 53 to 67) in ...
more infohttps://www.wikigenes.org/e/mesh/e/7619.html

Confidence Interval for a Population MeanConfidence Interval for a Population Mean

A) The 90% confidence interval for B) The 95% confidence interval for C) The 99% confidence interval for (1) Given. = 5.98. = ... is called the confidence coefficient or confidence level and the interval, , is called the confidence interval for .. Note that ... 99% confidence interval (The correct value of t for a 99% confidence interval with 9 degrees of freedom is 3.2498). 119 3.2498 ... An approximate confidence interval for is given by:. Components of an interval estimate. This is the general form for an ...
more infohttps://www.kean.edu/~fosborne/bstat/06amean.html

Confidence Intervals for fMRI Activation MapsConfidence Intervals for fMRI Activation Maps

Neuroimaging activation maps typically color voxels to indicate whether the blood oxygen level-dependent (BOLD) signals measured among two or more experimental conditions differ significantly at that location. This data presentation, however, omits information critical for interpretation of experimental results. First, no information is represented about trends at voxels that do not pass the statistical test. Second, no information is given about the range of probable effect sizes at voxels that do pass the statistical test. This leads to a fundamental error in interpreting activation maps by naïve viewers, where it is assumed that colored,
more infohttp://journals.plos.org/plosone/article?id=10.1371/journal.pone.0082419

Jones et al [1998]: Confidence Intervals « Climate AuditJones et al [1998]: Confidence Intervals « Climate Audit

... which purports to provide confidence intervals for the J98 reconstruction (blue). Theres (at least) one really strange feature ... If this is not the case then the confidence interval of average annual temperature IMHO is as big as 1 Kelvin, being the ... Jones et al [1998]: Confidence Intervals. Im working away at Jones et al [1998]. Heres an interesting diagram from Jones et ... If you have spurious regression, you have spurious confidence intervals. In the case of MBH, as far as I can tell, they ...
more infohttps://climateaudit.org/2005/10/26/jones-et-al-1998-confidence-intervals/?like=1&source=post_flair&_wpnonce=9e4b539e19

Jones et al [1998]: Confidence Intervals « Climate AuditJones et al [1998]: Confidence Intervals « Climate Audit

... which purports to provide confidence intervals for the J98 reconstruction (blue). Theres (at least) one really strange feature ... If this is not the case then the confidence interval of average annual temperature IMHO is as big as 1 Kelvin, being the ... Jones et al [1998]: Confidence Intervals. Im working away at Jones et al [1998]. Heres an interesting diagram from Jones et ... If you have spurious regression, you have spurious confidence intervals. In the case of MBH, as far as I can tell, they ...
more infohttps://climateaudit.org/2005/10/26/jones-et-al-1998-confidence-intervals/

Confidence intervals for the number needed to treat | The BMJConfidence intervals for the number needed to treat | The BMJ

Confidence intervals for the number needed to treat BMJ 1998; 317 :1309 ... Reanalyzing a Randomized Controlled Trial of Combination Antidepressant Treatment With Mirtazapine: Confidence Intervals ... Missing the point (estimate)? Confidence intervals for the number needed to treat ... Confidence intervals for the number needed to treat. BMJ 1998; 317 doi: https://doi.org/10.1136/bmj.317.7168.1309 (Published 07 ...
more infohttp://www.bmj.com/content/317/7168/1309/related

Confidence Interval Example - Measures of Association | CourseraConfidence Interval Example - Measures of Association | Coursera

This module introduces measures of association and confidence intervals. Learn online and earn valuable ... ... Confidence Interval Example. To view this video please enable JavaScript, and consider upgrading to a web browser that supports ... This module introduces measures of association and confidence intervals.. Confidence Intervals9:11 ...
more infohttps://www.coursera.org/lecture/epidemiology/confidence-interval-example-2e3JF

confidence interval | plus.maths.orgconfidence interval | plus.maths.org

How many people died? Its one of the first questions asked in a war or violent conflict, but its one of the hardest to answer. In the chaos of war many deaths go unrecorded and all sides have an interest in distorting the figures. The best we can do is come up with estimates, but the trouble is that different statistical methods for doing this can produce vastly different results . So how do we know how different methods compare?. ...
more infohttps://plus.maths.org/content/category/tags/confidence-interval

Re: [R-sig-phylo] Confidence intervals for B in ape::binaryPGLMMRe: [R-sig-phylo] Confidence intervals for B in ape::binaryPGLMM

... Emmanuel Paradis Fri, 22 Sep 2017 02:56:33 -0700 ... However, can I calculate confidence intervals for the regression coefficients? One way to get some numbers is to define the ... R-sig-phylo] Confidence intervals for B in ape::binar... Wouter van der Bijl ... Re: [R-sig-phylo] Confidence intervals for B ... Wouter van der Bijl ...
more infohttps://www.mail-archive.com/[email protected]/msg05074.html

Re: [R-sig-phylo] Confidence intervals for B in ape::binaryPGLMMRe: [R-sig-phylo] Confidence intervals for B in ape::binaryPGLMM

... Wouter van der Bijl Fri, 22 Sep 2017 04:22:03 -0700 ... However, can I calculate confidence intervals for the regression coefficients? One way to get some numbers is to define the ... R-sig-phylo] Confidence intervals for B in ape::binar... Wouter van der Bijl ... Re: [R-sig-phylo] Confidence intervals for B in a... Emmanuel Paradis ...
more infohttps://www.mail-archive.com/[email protected]/msg05075.html

Confidence Interval for Regression SlopeConfidence Interval for Regression Slope

The confidence interval is computed using where t is the critical value of the t statistic and Sb is the standard error in the ... Regression Analysis - Confidence Interval of the Slope. It is common in science and engineering to make a graph of experimental ... Often we need to report the slope with a confidence interval. For example, we may need to report the value of the slope is 1.23 ... If we were to use a table to look up t at the 95% confidence level, we would look the t value corresponding to a = (1-95%) = ...
more infohttps://people.stfx.ca/bliengme/ExcelTips/RegressionSlopeConfidence.htm

Robust confidence intervals - WikipediaRobust confidence intervals - Wikipedia

In statistics a robust confidence interval is a robust modification of confidence intervals, meaning that one modifies the non- ... Before trusting the results of 100 objects weighed just three times each to have confidence intervals calculated from σ, it is ... The 200 extra weighings served only to detect and correct for operator error and did nothing to improve the confidence interval ... He could then use a bootstrap calculation to determine a confidence interval narrower than that calculated from σ, and so ...
more infohttps://en.wikipedia.org/wiki/Robust_confidence_intervals

CDC - U.S. Cancer Statistics Technical Notes - Statistical Methods: Confidence Intervals - NPCR - CancerCDC - U.S. Cancer Statistics Technical Notes - Statistical Methods: Confidence Intervals - NPCR - Cancer

Explains how confidence intervals are calculated in United States Cancer Statistics. ... Confidence intervals reflect the range of variation in the estimation of the cancer rates. The width of a confidence interval ... Modified Gamma Intervals. Confidence intervals that are expected to include the true underlying rate 95% of the time are used ... Confidence intervals for directly standardized rates: a method based on the gamma distribution.external icon Statistics in ...
more infohttps://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/confidence.htm
  • The use of overlapping confidence intervals to test for statistically significant differences between two rates presented in the Data Visualizations tool is discouraged because the practice fails to detect significant differences more frequently than standard hypothesis testing. (cdc.gov)
  • Inference on Regressions with Interval Data on a Regressor or Outcome ," Econometrica , Econometric Society, vol. 70(2), pages 519-546, March. (repec.org)
  • Here's an interesting diagram from Jones et al [Science, 2001] , which purports to provide confidence intervals for the J98 reconstruction (blue). (climateaudit.org)
  • This entry was written by Steve McIntyre , posted on Oct 26, 2005 at 8:36 AM , filed under Jones et al 1998 , Multiproxy Studies and tagged CI , confidence intervals . (climateaudit.org)
  • We show that these two types of interval estimate are different in practice, the latter in general being shorter. (repec.org)
  • 7 Whereas P -values or "S" (significant) and "NS" (not significant) once were reported exclusively, the reporting of confidence intervals has now become accepted practice, with or without P- value accompaniment. (lww.com)
  • In practice, however, we select one random sample and generate one confidence interval, which may or may not contain the true mean. (bu.edu)
  • More strictly speaking, the confidence level represents the frequency (i.e. the proportion) of possible confidence intervals that contain the true value of the unknown population parameter. (wikipedia.org)
  • Confidence intervals consist of a range of potential values of the unknown population parameter . (wikipedia.org)
  • An interval estimate provides more information about a population characteristic than does a point estimate and therefore provides a confidence level for the estimate. (lynda.com)
  • With the information provided and confidence intervals overlapping, it is not reasonable to imply the means in the population are different. (bmj.com)
  • This module introduces measures of association and confidence intervals. (coursera.org)
  • Smithson first introduces the basis of the confidence interval framework and then provides the criteria for "best" confidence intervals, along with the trade-offs between confidence and precision. (sagepub.com)
  • Confidence intervals for directly standardized rates: a method based on the gamma distribution. (cdc.gov)
  • A complication arises from the fact that the simplest version of the proposed interval is discontinuous in the limit case of point identification, leading to coverage rates that are not uniform in important subsets of the parameter space. (repec.org)
  • Can someone please point me to the right command or tell me how to produce the confidence intervals? (stata.com)
  • Four, we should get serious about precision and look for narrow confidence intervals instead of low P -values to identify results that are least influenced by random error. (lww.com)
  • Nelson (1995) presents a methodology to obtain asymptotic confidence intervals for the cost and the number of cumulative recurrent events. (scielo.br)
  • contains the lower and upper 95% confidence interval boundaries for the mu parameter, and column 2 contains the boundaries for the sigma parameter. (mathworks.com)
  • In addition, the confidence intervals do not account for systematic (in other words, nonrandom) biases in the incidence rates. (cdc.gov)
  • Low P-Values or Narrow Confidence Intervals: Which Are More. (lww.com)