title: Sample size calculations for prevalent cohort designs.
abstract: Cross-sectional prevalent cohort design has drawn considerable interests in the studies of association between risk factors and time-to-event outcome. The sampling scheme in such design gives rise to length-biased data that require specialized analysis strategy but can improve study efficiency. The power and sample size calculation methods are however lacking for studies with prevalent cohort design, and using the formula developed for traditional survival data may overestimate sample size. We derive the sample size formulas that are appropriate for the design of cross-sectional prevalent cohort studies, under the assumptions of exponentially distributed event time and uniform follow-up for cross-sectional prevalent cohort design. We perform numerical and simulation studies to compare the sample size requirements for achieving the same power between prevalent cohort and incident cohort designs. We also use a large prospective prevalent cohort study to demonstrate the procedure. Using rigorous designs and proper analysis tools, the prospective prevalent cohort design can be more efficient than the incident cohort design with the same total sample sizes and study durations.
authors.affiliation: 1 Division of Biostatistics, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Liu
authors.affiliation: 2 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA.
authors.forename: Yu
authors.initials: Y
authors.lastname: Shen
authors.affiliation: 2 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Ning
authors.affiliation: 3 Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Qin
mesh_terms: D015982:Bias; D002170:Canada / Q000453:epidemiology; D015331:Cohort Studies; D003430:Cross-Sectional Studies / Q000379:methods*; D003704:Dementia / Q000175:diagnosis / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D015995:Prevalence; D011446:Prospective Studies; D012107:Research Design; D018401:Sample Size
keywords: incident cohort design; length-biased data; prevalent cohort design; sample size determination; survival data
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