title: HGLM versus conditional estimators for the analysis of clustered binary data.
abstract: Clustered binary data arise frequently in medical research such as cross-over clinical trials and twin studies. For the analysis of such data either a random-effects model or a conditional likelihood approach can be used. In this paper, we compare numerically the random-effects model estimator and the conditional likelihood estimator and discuss their relative merits for the analysis of binary data.
authors.affiliation: Department of Information and Statistics, Daejeon University, Daejeon 300-716, Korea.
authors.forename: Weechang
authors.initials: W
authors.lastname: Kang
authors.forename: Moo-Song
authors.initials: MS
authors.lastname: Lee
authors.forename: Youngjo
authors.initials: Y
authors.lastname: Lee
mesh_terms: D000367:Age Factors; D002986:Clinical Trials as Topic / Q000379:methods; D016000:Cluster Analysis; D018592:Cross-Over Studies; D003627:Data Interpretation, Statistical; D004358:Drug Therapy; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D012737:Sex Factors; D012871:Skin Diseases / Q000188:drug therapy; D018433:Twin Studies as Topic / Q000379:methods