Epidemiology is the study and analysis of the distribution, patterns and determinants of health and disease conditions in a defined population.
In statistical hypothesis testing, a result has statistical significance when a result at least as "extreme" would be very infrequent if the null hypothesis were true. More precisely, a study's defined significance level, denoted by , is the probability of the study rejecting the null hypothesis, given that the null hypothesis is true; and the p-value of a result, , is the probability of obtaining a result at least as extreme, given that the null hypothesis is true. The result is statistically significant, by the standards of the study, when . The significance level for a study is chosen before data collection, and is typically set to 5% or much lower—depending on the field of study.
The science of epidemiology has matured significantly from the times of Hippocrates, Semmelweis and John Snow. The techniques for gathering and analyzing epidemiological data vary depending on the type of disease being monitored but each study will have overarching similarities.
In pathology, pathogenesis is the process by which a disease or disorder develops. It can include factors which contribute not only to the onset of the disease or disorder, but also to its progression and maintenance. The word comes from Ancient Greek πάθος (pathos) 'suffering, disease', and γένεσις (genesis) 'creation'.
A case–control study is a type of observational study in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute. Case–control studies are often used to identify factors that may contribute to a medical condition by comparing subjects who have the condition with patients who do not have the condition but are otherwise similar. They require fewer resources but provide less evidence for causal inference than a randomized controlled trial. A case–control study is often used to produce an odds ratio. Some statistical methods make it possible to use a case–control study to also estimate relative risk, risk differences, and other quantities.
In causal inference, a confounder is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations. The existence of confounders is an important quantitative explanation why correlation does not imply causation. Some notations are explicitly designed to identify the existence, possible existence, or non-existence of confounders in causal relationships between elements of a system.
Spatial epidemiology is a subfield of epidemiology focused on the study of the spatial distribution of health outcomes; it is closely related to health geography.
Molecular epidemiology is a branch of epidemiology and medical science that focuses on the contribution of potential genetic and environmental risk factors, identified at the molecular level, to the etiology, distribution and prevention of disease within families and across populations. This field has emerged from the integration of molecular biology into traditional epidemiological research. Molecular epidemiology improves our understanding of the pathogenesis of disease by identifying specific pathways, molecules and genes that influence the risk of developing disease. More broadly, it seeks to establish understanding of how the interactions between genetic traits and environmental exposures result in disease.
John P. A. Ioannidis is a Greek-American physician-scientist, writer and Stanford University professor who has made contributions to evidence-based medicine, epidemiology, and clinical research. Ioannidis studies scientific research itself, meta-research primarily in clinical medicine and the social sciences.
The Bradford Hill criteria, otherwise known as Hill's criteria for causation, are a group of nine principles that can be useful in establishing epidemiologic evidence of a causal relationship between a presumed cause and an observed effect and have been widely used in public health research. They were established in 1965 by the English epidemiologist Sir Austin Bradford Hill.
Cause, also known as etiology and aetiology, is the reason or origination of something.
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The study of why things occur is called etiology, and can be described using the language of scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning.
Estimation statistics, or simply estimation, is a data analysis framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results. It complements hypothesis testing approaches such as null hypothesis significance testing (NHST), by going beyond the question is an effect present or not, and provides information about how large an effect is. Estimation statistics is sometimes referred to as the new statistics.
Abraham Morris Lilienfeld was an American epidemiologist and professor at the Johns Hopkins School of Hygiene and Public Health. He is known for his work in expanding epidemiology to focus on chronic diseases as well as infectious ones.
Sudipto Banerjee is an Indian-American statistician best known for his work on Bayesian hierarchical modeling and inference for spatial data analysis. He is Professor of Biostatistics and Senior Associate Dean in the School of Public Health at the University of California, Los Angeles. He served as the Chair of the Department of Biostatistics at UCLA from 2014 through 2023. He served as the elected President of the International Society for Bayesian Analysis in 2022.
In statistics, dichotomous thinking or binary thinking is the process of seeing a discontinuity in the possible values that a p-value can take during null hypothesis significance testing: it is either above the significance threshold or below. When applying dichotomous thinking, a first p-value of 0.0499 will be interpreted the same as a p-value of 0.0001 while a second p-value of 0.0501 will be interpreted the same as a p-value of 0.7. The fact that first and second p-values are mathematically very close is thus completely disregarded and values of p are not considered as continuous but are interpreted dichotomously with respect to the significance threshold. A common measure of dichotomous thinking is the cliff effect. A reason to avoid dichotomous thinking is that p-values and other statistics naturally change from study to study due to random variation alone; decisions about refutation or support of a scientific hypothesis based on a result from a single study are therefore not reliable.
Tyler J. VanderWeele is the John L. Loeb and Frances Lehman Loeb Professor of Epidemiology in the Departments of Epidemiology and Biostatistics at the Harvard T.H. Chan School of Public Health. He is also the co-director of Harvard University's Initiative on Health, Religion and Spirituality, the director of their Human Flourishing Program, and a faculty affiliate of the Harvard Institute for Quantitative Social Science. He holds degrees from the University of Oxford, University of Pennsylvania, and Harvard University in mathematics, philosophy, theology, finance, and biostatistics.
Valentin Amrhein is a German-Swiss professor of zoology at the University of Basel and science journalist. Together with Sander Greenland and others, he is a critic of significance thresholds in science and he draws attention to misunderstandings of p-values. He is author of a comment in the journal Nature on statistical significance that had the highest online attention score of all research outputs ever screened by Altmetric.
Babette Anne Brumback is an American biostatistician known for her work on causal inference. She is a professor of biostatistics at the University of Florida.
In the field of epidemiology, the causal mechanisms responsible for diseases can be understood using the causal pie model.This conceptual model was introduced by Ken Rothman to communicate how constellations of component causes can lead to a sufficient cause to lead to a condition of interest and that reflection on these sets could improve epidemiological study design. A set of proposed causal mechanisms are represented as pie charts where each pie in the diagram represent a theoretical causal mechanism for a given disease, which is also called a sufficient cause. Each pie is made up of many component factors, otherwise known as component causes represented by sectors in the diagram. In this framework, each component cause represents an event or condition required for a given disease or outcome. A component cause that appears in every pie is called a necessary cause as the outcome cannot occur without it.
