• Correlated data;
  • Large cohort studies;
  • Nested case–control studies;
  • Power calculations;
  • Survival models


A large number of factors can affect the statistical power and bias of analyses of data from large cohort studies, including misclassification, correlated data, follow-up time, prevalence of the risk factor of interest, and prevalence of the outcome. This paper presents a method for simulating cohorts where individual's risk is correlated within communities, recruitment is staggered over time, and outcomes are observed after different follow-up periods. Covariates and outcomes are misclassified, and Cox proportional hazards models are fit with a community-level frailty term. The effect on study power of varying effect sizes, prevalences, correlation, and misclassification are explored, as well as varying the proportion of controls in nested case–control studies.