Sample size calculations for prevalent cohort designs

Hao Liu, Yu Shen, Jing Ning, Jing Qin

Research output: Contribution to journalArticle

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)280-291
Number of pages12
JournalStatistical Methods in Medical Research
Volume26
Issue number1
DOIs
StatePublished - Feb 1 2017
Externally publishedYes

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Keywords

  • incident cohort design
  • length-biased data
  • prevalent cohort design
  • sample size determination
  • survival data

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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