Simulation-based power calculations for large cohort studies.
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.
McMaster University, Canada. patrick.brown@utoronto.ca
Patrick
P
Brown
Hedy
H
Jiang
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D016022:Case-Control Studies; D015331:Cohort Studies; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D016012:Poisson Distribution; D016016:Proportional Hazards Models; D012306:Risk; D016019:Survival Analysis; D013997:Time Factors