title: Gauss or Bernoulli? A Monte Carlo comparison of the performance of the linear mixed-model and the logistic mixed-model analyses in simulated community trials with a dichotomous outcome variable at the individual level.
abstract: This Monte Carlo study compares performance of the linear and the logistic mixed-model analyses of simulated community trials having event rates of 37%, 13%, or 5%, intraclass correlations between 0.01 and 0.05, and 17 or 5 denominator degrees of freedom. Type I or Type II error rates showed no essential difference between the two analysis methods. They showed depressed error rates when the event rate or the denominator degrees of freedom were small. The authors conclude that in studies with adequate denominator degrees of freedom, the researcher may use either method of analysis but should accept negative estimates of components of variance to avoid depression of error rates.
authors: Division of Epidemiology, School of Public Health, University of Minnesota, Minneapolis 55454-1015, USA.
authors: P J
authors: PJ
authors: Hannan
authors: D M
authors: DM
authors: Murray
mesh_terms: D000704:Analysis of Variance; D015982:Bias; D016010:Binomial Distribution; D003153:Community Health Services / Q000592:standards*; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D009010:Monte Carlo Method; D016011:Normal Distribution; D015397:Program Evaluation / Q000379:methods*; D015203:Reproducibility of Results