title: Sample size calculation for before-after experiments with partially overlapping cohorts.
abstract: 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.
authors.affiliation: Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, United States. Electronic address: song.zhang@utsouthwestern.edu.
authors.forename: Song
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Department of Statistical Science, Southern Methodist University, Dallas, TX, United States. Electronic address: jcao@mail.smu.edu.
authors.forename: Jing
authors.initials: J
authors.lastname: Cao
authors.affiliation: Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas TX 75390-9066, United States. Electronic address: chul.ahn@utsouthwestern.edu.
authors.forename: Chul
authors.initials: C
authors.lastname: Ahn
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D012107:Research Design; D018401:Sample Size
keywords: Before–after study; Binary outcome; Clinical trial; Experimental design; Sample size