Sample size estimation for a two-group comparison of repeated count outcomes using GEE

Ying Lou, Jing Cao, Song Zhang, Chul Ahn

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Randomized clinical trials with count measurements as the primary outcome are common in various medical areas such as seizure counts in epilepsy trials, or relapse counts in multiple sclerosis trials. Controlled clinical trials frequently use a conventional parallel-group design that assigns subjects randomly to one of two treatment groups and repeatedly evaluates them at baseline and intervals across a treatment period of a fixed duration. The primary interest is to compare the rates of change between treatment groups. Generalized estimating equations (GEEs) have been widely used to compare rates of change between treatment groups because of its robustness to misspecification of the true correlation structure. In this paper, we derive a sample size formula for comparing the rates of change between two groups in a repeatedly measured count outcome using GEE. The sample size formula incorporates general missing patterns such as independent missing and monotone missing, and general correlation structures such as AR(1) and compound symmetry (CS). The performance of the sample size formula is evaluated through simulation studies. Sample size estimation is illustrated by a clinical trial example from epilepsy.

Original languageEnglish (US)
Pages (from-to)6743-6753
Number of pages11
JournalCommunications in Statistics - Theory and Methods
Volume46
Issue number14
DOIs
StatePublished - Jul 18 2017

Keywords

  • Count outcome
  • GEE
  • missing data
  • repeated measurements
  • sample size

ASJC Scopus subject areas

  • Statistics and Probability

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