Simulation-based power calculations for large cohort studies

Authors

  • Patrick Brown,

    Corresponding author
    1. Population Studies and Surveillance, Cancer Care Ontario and Clinical Epidemiology and Biostatistics, McMaster University, Canada
    2. Dalla Lana School of Public Health, University of Toronto, Canada
    • Phone: +1-416-971-9800, Fax: +1-416-971-6888
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  • Hedy Jiang

    1. Population Studies and Surveillance, Cancer Care Ontario and Clinical Epidemiology and Biostatistics, McMaster University, Canada
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Abstract

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.

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