cohortenanalyses

Cohortenanalyses, often written as cohort analyses in English, refer to analytical approaches that study groups of individuals who share a defining characteristic (a cohort) over time to observe how outcomes develop. A cohort can be defined by birth year, enrollment date, exposure status, or another shared attribute. The primary goal is to assess temporal relationships and the incidence of outcomes within and between cohorts.

Cohort analyses encompass prospective and retrospective designs. In a prospective cohort study, researchers follow participants forward in time from exposure to outcome. In retrospective cohorts, historical data are used to reconstruct exposure and outcome timelines. Common analytic methods include survival analysis for time-to-event data, Kaplan-Meier curves, Cox proportional hazards models, and, for continuous or count outcomes, linear mixed models or generalized linear models. Longitudinal or panel data techniques are also employed to account for repeated measures within individuals.

Data for cohort analyses are typically longitudinal and may come from clinical registries, electronic health records, administrative databases, or survey panels. Key considerations include handling confounding, loss to follow-up (attrition), competing risks, and measurement error. Cohort analyses often focus on incidence rates, relative risks or hazard ratios, and subgroup analyses to explore effect modification.

Applications span disciplines. In epidemiology and public health, cohorts assess lead exposure, lifestyle factors, and disease risk. In economics or marketing, cohorts track behavior of groups defined by signup or launch dates to study retention, product uptake, or lifetime value. Well-designed cohort analyses provide insights into temporality and causal inference while acknowledging practical constraints and biases.