In case-control studies, investigators compare exposures between people with a particular disease outcome (cases) and people without that outcome (controls). Investigators aim to collect cases and controls that are representative of an underlying cohort or a cross-section of a population. That population can be defined geographically, but also more loosely as the catchment area of health care facilities. The case sample may be 100% or a large fraction of available cases, while the control sample usually is only a small fraction of the people who do not have the pertinent outcome. Controls represent the cohort or population of people from which the cases arose. Investigators calculate the ratio of the odds of exposures to putative causes of the disease among cases and controls. Depending on the sampling strategy for cases and controls and the nature of the population studied, the odds ratio obtained in a case-control study is interpreted as the risk ratio, rate ratio or (prevalence) odds ratio. The majority of published case-control studies sample open cohorts and so allow direct estimations of rate ratios.
A classic case-control study is Smoking and Carcinoma of the Lung by Richard Doll and A Bradford Hill which suggested a link between smoking and lung cancer.
In cohort study, one or more groups are closely monitored and outcomes measured over time. Researchers collect information about people and their exposures to factors which might affect their health at baseline, let time pass, and then measure the occurrence of pre-specified outcomes. Researchers commonly make contrasts between individuals who are exposed and not exposed to these factors, or among groups of individuals with different levels of exposure. Investigators may assess several different outcomes, and examine exposure and outcome variables at multiple points during follow-up. Some cohorts are closed - for example birth cohorts. They enrol a defined number of participants at the beginning of the study and follow them from that time forward, often at set intervals up to a fixed end date. Some are open cohorts - for example inhabitants of a town. This means people enter and leave the population at different points in time . Open cohorts change due to deaths, births, and migration, but the composition of the population with regard to variables such as age and gender may remain approximately constant, especially over a short period of time. In a closed cohort cumulative incidences (risks) and incidence rates can be estimated; when exposed and unexposed groups are compared, this leads to risk ratio or rate ratio estimates. Open cohorts estimate incidence rates and rate ratios.
A classic cohort study is The British Doctors' Cohort by Richard Doll and A Bradford Hill which confirmed the link between smoking and lung cancer.
In cross-sectional studies, investigators assess all individuals in a sample at the same point in time, often to examine the prevalence of exposures, risk factors or disease. Some cross-sectional studies are analytical and aim to quantify potential causal associations between exposures and disease. Such studies may be analysed like a cohort study by comparing disease prevalence between exposure groups. They may also be analysed like a case-control study by comparing the odds of exposure between groups with and without disease. A difficulty that can occur in any design but is particularly clear in cross-sectional studies is to establish that an exposure preceded the disease, although the time order of exposure and outcome may sometimes be clear. In a study in which the exposure variable is congenital or genetic, for example, we can be confident that the exposure preceded the disease, even if we are measuring both at the same time.