Sample size calculation for before–after experiments with partially overlapping cohorts

Song Zhang, Jing Cao, Chul Ahn

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


We investigate sample size calculation for before–after experiments where the outcome of interest is binary and the enrolled subjects contribute a mixed type of data: some subjects contribute complete pairs of before- and after-intervention outcomes, while some subjects contribute incomplete data (before-intervention only or after-intervention only). We use the GEE approach to derive a closed-form sample size formula by treating the incomplete observations as missing data in a generalized linear model. The impacts of various designing factors are appropriately accounted for in the sample size formula, including intervention effect, baseline response rate, within-subject correlation, and distribution of missing values in the before- and after-intervention periods. We illustrate sample size estimation using a real application example. We conduct simulation studies to demonstrate that the proposed sample size maintains the nominal power and type I error under a wide spectrum of trial configurations.

Original languageEnglish (US)
Pages (from-to)274-280
Number of pages7
JournalContemporary Clinical Trials
StatePublished - Jan 2018


  • Before–after study
  • Binary outcome
  • Clinical trial
  • Experimental design
  • Sample size

ASJC Scopus subject areas

  • Pharmacology (medical)


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