Biostatistics
Epidemiology
Biometry
Education, Dental, Graduate
Education, Graduate
Capacity Building
Education, Public Health Professional
Biomedical Research
Statistics as Topic
Data Interpretation, Statistical
Research Design
Encyclopedias as Topic
Chromosome Mapping
Quantitative Trait, Heritable
Crosses, Genetic
Human coronary artery remodeling, beginning and end of the atherosclerotic process. (1/257)
BACKGROUND, AIMS OF THE STUDY: The objective of the study was to relate the progress of coronary artery remodeling to the earliest stages of the atherosclerotic process. For this purpose, a mathematical model for description of dimensional change of the coronary artery wall and its constituent components was developed and applied. MATERIALS AND METHODS: The study used coronary artery samples randomly taken from each of 83 consecutive, unselected postmortems. All samples were routinely fixed and processed to paraffin for the preparation of right-angled, 5-micron sections, routinely stained and mounted for subsequent analysis. Computer assisted image analysis, using 32 systematic random, radial sampling lines, was used for interactive measurements of distance from centre of lumen to points defining intima, media and adventitia thickness along the radial intercept, which were subsequently tabled for analysis of variance, calculations of (group-vessel) means, and related to stage of pathology. RESULTS: Pre-atherosclerotic changes, before any localised changes in especially intima dimensions, are found, consisting of a process of gradual vascular widening, associated with temporally at least partly dissociated increases in width, which as a fraction of total vessel radius show a phased process. In these, the intima first increases, subsequently remains stable, and finally reduces in width proportionally to the increasing diameter. The media shows a similar initial increase, on average stabilising in the third phase after reaching a plateau value in the second. The adventitia, already increasing in phase 1, continues to increase in phase 2, accelerating in phase 3. The complex process, as found, occurs systematically in all vessels, is distributed circumferentially, and precedes the development of localised lesions of the intima. CONCLUSIONS: The findings suggest the existence of a diffuse complex of changes, consisting of a gradual vascular widening followed by narrowing, with associated mural changes reflecting the atherosclerotic process. (+info)Empirical efficiency maximization: improved locally efficient covariate adjustment in randomized experiments and survival analysis. (2/257)
It has long been recognized that covariate adjustment can increase precision in randomized experiments, even when it is not strictly necessary. Adjustment is often straightforward when a discrete covariate partitions the sample into a handful of strata, but becomes more involved with even a single continuous covariate such as age. As randomized experiments remain a gold standard for scientific inquiry, and the information age facilitates a massive collection of baseline information, the longstanding problem of if and how to adjust for covariates is likely to engage investigators for the foreseeable future. In the locally efficient estimation approach introduced for general coarsened data structures by James Robins and collaborators, one first fits a relatively small working model, often with maximum likelihood, giving a nuisance parameter fit in an estimating equation for the parameter of interest. The usual advertisement is that the estimator will be asymptotically efficient if the working model is correct, but otherwise will still be consistent and asymptotically Gaussian. However, by applying standard likelihood-based fits to misspecified working models in covariate adjustment problems, one can poorly estimate the parameter of interest. We propose a new method, empirical efficiency maximization, to optimize the working model fit for the resulting parameter estimate. In addition to the randomized experiment setting, we show how our covariate adjustment procedure can be used in survival analysis applications. Numerical asymptotic efficiency calculations demonstrate gains relative to standard locally efficient estimators (+info)Empirical vs natural weighting in random effects meta-analysis. (3/257)
(+info)A novel approach to cancer staging: application to esophageal cancer. (4/257)
(+info)Estimation and inference for case-control studies with multiple non-gold standard exposure assessments: with an occupational health application. (5/257)
(+info)Variable selection and dependency networks for genomewide data. (6/257)
(+info)A semiparametric 2-part mixed-effects heteroscedastic transformation model for correlated right-skewed semicontinuous data. (7/257)
(+info)Rank-based estimation in the {ell}1-regularized partly linear model for censored outcomes with application to integrated analyses of clinical predictors and gene expression data. (8/257)
(+info)Biostatistics is the application of statistics to a wide range of topics in biology, public health, and medicine. It involves the design, execution, analysis, and interpretation of statistical studies in these fields. Biostatisticians use various mathematical and statistical methods to analyze data from clinical trials, epidemiological studies, and other types of research in order to make inferences about populations and test hypotheses. They may also be involved in the development of new statistical methods for specific applications in biology and medicine.
The goals of biostatistics are to help researchers design valid and ethical studies, to ensure that data are collected and analyzed in a rigorous and unbiased manner, and to interpret the results of statistical analyses in the context of the underlying biological or medical questions. Biostatisticians may work closely with researchers in many different areas, including genetics, epidemiology, clinical trials, public health, and health services research.
Some specific tasks that biostatisticians might perform include:
* Designing studies and experiments to test hypotheses or answer research questions
* Developing sampling plans and determining sample sizes
* Collecting and managing data
* Performing statistical analyses using appropriate methods
* Interpreting the results of statistical analyses and drawing conclusions
* Communicating the results of statistical analyses to researchers, clinicians, and other stakeholders
Biostatistics is an important tool for advancing our understanding of biology and medicine, and for improving public health. It plays a key role in many areas of research, including the development of new drugs and therapies, the identification of risk factors for diseases, and the evaluation of public health interventions.
Epidemiology is the study of how often and why diseases occur in different groups of people and places. It is a key discipline in public health and informs policy decisions and evidence-based practices by identifying risk factors for disease and targets for preventive healthcare. Epidemiologists use various study designs, including observational studies, experiments, and surveys, to collect and analyze data on the distribution and determinants of diseases in populations. They seek to understand the causes of health outcomes and develop strategies to control or prevent adverse health events. The ultimate goal of epidemiology is to improve population health and eliminate health disparities.
Biometry, also known as biometrics, is the scientific study of measurements and statistical analysis of living organisms. In a medical context, biometry is often used to refer to the measurement and analysis of physical characteristics or features of the human body, such as height, weight, blood pressure, heart rate, and other physiological variables. These measurements can be used for a variety of purposes, including diagnosis, treatment planning, monitoring disease progression, and research.
In addition to physical measurements, biometry may also refer to the use of statistical methods to analyze biological data, such as genetic information or medical images. This type of analysis can help researchers and clinicians identify patterns and trends in large datasets, and make predictions about health outcomes or treatment responses.
Overall, biometry is an important tool in modern medicine, as it allows healthcare professionals to make more informed decisions based on data and evidence.
I'm sorry for any confusion, but "Schools, Health Occupations" is not a medical term or concept. It may refer to educational programs or institutions that focus on training individuals for various health care occupations, such as nursing, dental hygiene, or medical assisting. However, it does not have a specific medical definition. If you have any questions related to medical terminology or concepts, I'd be happy to help!
"Dental, Graduate Education" refers to the post-baccalaureate programs of study and training that lead to an advanced degree in the field of dentistry. These programs are designed to prepare students for specialized dental practice, research, or teaching careers. Examples of graduate dental degrees include:
1. Doctor of Dental Surgery (DDS): A professional doctoral degree that qualifies the graduate to practice general dentistry.
2. Doctor of Medical Dentistry (DMD): A professional doctoral degree equivalent to the DDS; awarded by some universities in the United States and several other countries.
3. Master of Science (MS) in Dentistry: An academic master's degree focused on research, teaching, or advanced clinical practice in a specific dental discipline.
4. Doctor of Philosophy (PhD) in Dental Sciences: A research-oriented doctoral degree that prepares students for careers in academia, research institutions, or the dental industry.
5. Specialty Training Programs: Postgraduate residency programs that provide advanced training in one of the nine recognized dental specialties, such as orthodontics, oral and maxillofacial surgery, or pediatric dentistry. These programs typically lead to a certificate or a master's degree in the respective specialty area.
Graduate dental education usually involves a combination of classroom instruction, laboratory work, clinical experience, and research. Admission to these programs typically requires a DDS or DMD degree from an accredited dental school and satisfactory scores on the Dental Admission Test (DAT).
I'm sorry for any confusion, but "Personnel Management" and "medical definition" are two separate concepts that don't typically intersect.
Personnel management is a function of management concerned with hiring, training, developing, compensating, and motivating employees, as well as maintaining appropriate records and ensuring legal compliance. It's a crucial aspect of human resource management in any organization, including healthcare institutions.
However, if you're looking for a medical term related to the management of personnel in a healthcare setting, you might consider "Healthcare Human Resources Management" or "Clinical Workforce Management." These terms refer to the specific application of personnel management principles and practices within the context of healthcare organizations.
Graduate education typically refers to educational programs beyond the undergraduate level that lead to an advanced degree, such as a master's, doctoral, or professional degree. These programs usually require completion of a Bachelor's degree as a prerequisite and involve more specialized and in-depth study in a particular field. Graduate education may include coursework, research, examinations, and the completion of a thesis or dissertation. The specific requirements for graduate education vary depending on the field of study and the institution offering the degree program.
Capacity building, in the context of healthcare and medicine, refers to the process of developing and strengthening the skills, knowledge, systems, and resources needed to improve the delivery and accessibility of healthcare services. This can involve a range of activities, including training and education for healthcare professionals, improving infrastructure and technology, establishing policies and guidelines, and promoting community engagement and participation. The goal of capacity building is to enhance the overall performance and sustainability of healthcare systems, ultimately leading to better health outcomes for individuals and populations.
A public health professional is a trained and educated individual who works to improve the health and well-being of communities and populations through education, research, policy development, and advocacy. A public health professional in the field of education may work in various settings such as universities, colleges, public health departments, non-profit organizations, or government agencies.
Their responsibilities typically include:
1. Developing and implementing educational programs to promote healthy behaviors and prevent disease and injury.
2. Conducting research to identify the health needs and concerns of communities and developing strategies to address them.
3. Advocating for policies and practices that support public health and promote health equity.
4. Collaborating with other professionals, such as healthcare providers, community leaders, and policymakers, to develop and implement effective public health interventions.
5. Evaluating the impact of public health programs and using data to inform decision-making and improve outcomes.
To become a public health professional in education, one typically needs to have at least a master's degree in public health or a related field, such as health education, health promotion, or health services administration. Some positions may require a doctoral degree or additional certifications. Relevant work experience, such as internships or volunteer work, is also valuable for gaining practical skills and making professional connections.
In the context of medical education, a curriculum refers to the planned and organized sequence of experiences and learning opportunities designed to achieve specific educational goals and objectives. It outlines the knowledge, skills, and attitudes that medical students or trainees are expected to acquire during their training program. The curriculum may include various components such as lectures, small group discussions, clinical rotations, simulations, and other experiential learning activities. It is typically developed and implemented by medical education experts and faculty members in consultation with stakeholders, including learners, practitioners, and patients.
Biomedical research is a branch of scientific research that involves the study of biological processes and diseases in order to develop new treatments and therapies. This type of research often involves the use of laboratory techniques, such as cell culture and genetic engineering, as well as clinical trials in humans. The goal of biomedical research is to advance our understanding of how living organisms function and to find ways to prevent and treat various medical conditions. It encompasses a wide range of disciplines, including molecular biology, genetics, immunology, pharmacology, and neuroscience, among others. Ultimately, the aim of biomedical research is to improve human health and well-being.
Statistics, as a topic in the context of medicine and healthcare, refers to the scientific discipline that involves the collection, analysis, interpretation, and presentation of numerical data or quantifiable data in a meaningful and organized manner. It employs mathematical theories and models to draw conclusions, make predictions, and support evidence-based decision-making in various areas of medical research and practice.
Some key concepts and methods in medical statistics include:
1. Descriptive Statistics: Summarizing and visualizing data through measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation).
2. Inferential Statistics: Drawing conclusions about a population based on a sample using hypothesis testing, confidence intervals, and statistical modeling.
3. Probability Theory: Quantifying the likelihood of events or outcomes in medical scenarios, such as diagnostic tests' sensitivity and specificity.
4. Study Designs: Planning and implementing various research study designs, including randomized controlled trials (RCTs), cohort studies, case-control studies, and cross-sectional surveys.
5. Sampling Methods: Selecting a representative sample from a population to ensure the validity and generalizability of research findings.
6. Multivariate Analysis: Examining the relationships between multiple variables simultaneously using techniques like regression analysis, factor analysis, or cluster analysis.
7. Survival Analysis: Analyzing time-to-event data, such as survival rates in clinical trials or disease progression.
8. Meta-Analysis: Systematically synthesizing and summarizing the results of multiple studies to provide a comprehensive understanding of a research question.
9. Biostatistics: A subfield of statistics that focuses on applying statistical methods to biological data, including medical research.
10. Epidemiology: The study of disease patterns in populations, which often relies on statistical methods for data analysis and interpretation.
Medical statistics is essential for evidence-based medicine, clinical decision-making, public health policy, and healthcare management. It helps researchers and practitioners evaluate the effectiveness and safety of medical interventions, assess risk factors and outcomes associated with diseases or treatments, and monitor trends in population health.
Statistical data interpretation involves analyzing and interpreting numerical data in order to identify trends, patterns, and relationships. This process often involves the use of statistical methods and tools to organize, summarize, and draw conclusions from the data. The goal is to extract meaningful insights that can inform decision-making, hypothesis testing, or further research.
In medical contexts, statistical data interpretation is used to analyze and make sense of large sets of clinical data, such as patient outcomes, treatment effectiveness, or disease prevalence. This information can help healthcare professionals and researchers better understand the relationships between various factors that impact health outcomes, develop more effective treatments, and identify areas for further study.
Some common statistical methods used in data interpretation include descriptive statistics (e.g., mean, median, mode), inferential statistics (e.g., hypothesis testing, confidence intervals), and regression analysis (e.g., linear, logistic). These methods can help medical professionals identify patterns and trends in the data, assess the significance of their findings, and make evidence-based recommendations for patient care or public health policy.
A research design in medical or healthcare research is a systematic plan that guides the execution and reporting of research to address a specific research question or objective. It outlines the overall strategy for collecting, analyzing, and interpreting data to draw valid conclusions. The design includes details about the type of study (e.g., experimental, observational), sampling methods, data collection techniques, data analysis approaches, and any potential sources of bias or confounding that need to be controlled for. A well-defined research design helps ensure that the results are reliable, generalizable, and relevant to the research question, ultimately contributing to evidence-based practice in medicine and healthcare.
Quantitative Trait Loci (QTL) are regions of the genome that are associated with variation in quantitative traits, which are traits that vary continuously in a population and are influenced by multiple genes and environmental factors. QTLs can help to explain how genetic variations contribute to differences in complex traits such as height, blood pressure, or disease susceptibility.
Quantitative trait loci are identified through statistical analysis of genetic markers and trait values in experimental crosses between genetically distinct individuals, such as strains of mice or plants. The location of a QTL is inferred based on the pattern of linkage disequilibrium between genetic markers and the trait of interest. Once a QTL has been identified, further analysis can be conducted to identify the specific gene or genes responsible for the variation in the trait.
It's important to note that QTLs are not themselves genes, but rather genomic regions that contain one or more genes that contribute to the variation in a quantitative trait. Additionally, because QTLs are identified through statistical analysis, they represent probabilistic estimates of the location of genetic factors influencing a trait and may encompass large genomic regions containing multiple genes. Therefore, additional research is often required to fine-map and identify the specific genes responsible for the variation in the trait.
An encyclopedia is a comprehensive reference work containing articles on various topics, usually arranged in alphabetical order. In the context of medicine, a medical encyclopedia is a collection of articles that provide information about a wide range of medical topics, including diseases and conditions, treatments, tests, procedures, and anatomy and physiology. Medical encyclopedias may be published in print or electronic formats and are often used as a starting point for researching medical topics. They can provide reliable and accurate information on medical subjects, making them useful resources for healthcare professionals, students, and patients alike. Some well-known examples of medical encyclopedias include the Merck Manual and the Stedman's Medical Dictionary.
Chromosome mapping, also known as physical mapping, is the process of determining the location and order of specific genes or genetic markers on a chromosome. This is typically done by using various laboratory techniques to identify landmarks along the chromosome, such as restriction enzyme cutting sites or patterns of DNA sequence repeats. The resulting map provides important information about the organization and structure of the genome, and can be used for a variety of purposes, including identifying the location of genes associated with genetic diseases, studying evolutionary relationships between organisms, and developing genetic markers for use in breeding or forensic applications.
A quantitative trait is a phenotypic characteristic that can be measured and displays continuous variation, meaning it can take on any value within a range. Examples include height, blood pressure, or biochemical measurements like cholesterol levels. These traits are usually influenced by the combined effects of multiple genes (polygenic inheritance) as well as environmental factors.
Heritability, in the context of genetics, refers to the proportion of variation in a trait that can be attributed to genetic differences among individuals in a population. It is estimated using statistical methods and ranges from 0 to 1, with higher values indicating a greater contribution of genetics to the observed phenotypic variance.
Therefore, a heritable quantitative trait would be a phenotype that shows continuous variation, influenced by multiple genes and environmental factors, and for which a significant portion of the observed variation can be attributed to genetic differences among individuals in a population.
"Genetic crosses" refer to the breeding of individuals with different genetic characteristics to produce offspring with specific combinations of traits. This process is commonly used in genetics research to study the inheritance patterns and function of specific genes.
There are several types of genetic crosses, including:
1. Monohybrid cross: A cross between two individuals that differ in the expression of a single gene or trait.
2. Dihybrid cross: A cross between two individuals that differ in the expression of two genes or traits.
3. Backcross: A cross between an individual from a hybrid population and one of its parental lines.
4. Testcross: A cross between an individual with unknown genotype and a homozygous recessive individual.
5. Reciprocal cross: A cross in which the male and female parents are reversed to determine if there is any effect of sex on the expression of the trait.
These genetic crosses help researchers to understand the mode of inheritance, linkage, recombination, and other genetic phenomena.
Genetic markers are specific segments of DNA that are used in genetic mapping and genotyping to identify specific genetic locations, diseases, or traits. They can be composed of short tandem repeats (STRs), single nucleotide polymorphisms (SNPs), restriction fragment length polymorphisms (RFLPs), or variable number tandem repeats (VNTRs). These markers are useful in various fields such as genetic research, medical diagnostics, forensic science, and breeding programs. They can help to track inheritance patterns, identify genetic predispositions to diseases, and solve crimes by linking biological evidence to suspects or victims.