withincohort

Withincohort is a term used in epidemiology and biostatistics to describe analyses conducted within a defined cohort. It refers to methods and metrics that focus on variation observed inside the cohort over time, rather than comparisons between different cohorts. In a withincohort analysis, researchers examine how changes in exposures or outcomes within the same individuals relate to each other, leveraging longitudinal data to control for confounding by fixed, unobserved characteristics.

Common approaches include fixed-effects models that estimate within-individual associations by removing all time-invariant between-individual differences, and within-subject centering where exposures are centered around a subject's own mean. Linear mixed models with random intercepts can also separate within- and between-subject variation. These methods help isolate causal-like within-person relationships when appropriate, assuming no time-varying confounding.

Applications span longitudinal cohort studies, clinical cohorts, and social epidemiology, where the goal is to understand how changes in exposures over time relate to changes in outcomes. Withincohort analyses are particularly valuable when between-subject heterogeneity is substantial and unmeasured confounding is a concern.

Limitations include reliance on repeated measures, potential bias from time-varying confounders, measurement error, and reduced generalizability to other populations. As with any observational approach, results from withincohort analyses should be interpreted alongside other evidence and, when possible, triangulated with causal designs.