Performance of a mixed effects logistic regression model for binary outcomes with unequal cluster size

Moonseong Heo, Andrew C. Leon

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

When a clustered randomized controlled trial is considered at a design stage of a clinical trial, it is useful to consider the consequences of unequal cluster size (i.e., sample size per cluster). Furthermore, the assumption of independence of observations within cluster does not hold, of course, because the subjects share the same cluster. Moreover, when the clustered outcomes are binary, a mixed effect logistic regression model is applicable. This article compares the performance of a maximum likelihood estimation of the mixed effects logistic regression model with equal and unequal cluster sizes. This was evaluated in terms of type I error rate, power, bias, and standard error through computer simulations that varied treatment effect, number of clusters, and intracluster correlation coefficients. The results show that the performance of the mixed effects logistic regression model is very similar, regardless of inequality in cluster size. This is illustrated using data from the Prevention Of Suicide in Primary care Elderly: Collaborative Trial (PROSPECT) study.

Original languageEnglish (US)
Pages (from-to)513-526
Number of pages14
JournalJournal of Biopharmaceutical Statistics
Volume15
Issue number3
DOIs
Publication statusPublished - May 27 2005

    Fingerprint

Keywords

  • Bias
  • Clustered binary observations
  • Clustered randomized controlled trials
  • ICC
  • Power
  • Type I error rate

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

Cite this