title: Performance of a mixed effects logistic regression model for binary outcomes with unequal cluster size.
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.
authors.affiliation: Department of Psychiatry, Weill Medical College of Cornell University, White Plains, NY 10605, USA. moh2002@med.cornell.edu
authors.forename: Moonseong
authors.initials: M
authors.lastname: Heo
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Leon
mesh_terms: D000465:Algorithms; D015982:Bias; D016000:Cluster Analysis; D003198:Computer Simulation; D003866:Depressive Disorder / Q000188:drug therapy / Q000523:psychology; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D011338:Probability Theory; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data