HGLM versus conditional estimators for the analysis of clustered binary data

Stat Med. 2005 Mar 15;24(5):741-52. doi: 10.1002/sim.1772.

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.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Age Factors
  • Clinical Trials as Topic / methods
  • Cluster Analysis
  • Cross-Over Studies
  • Data Interpretation, Statistical*
  • Drug Therapy
  • Female
  • Humans
  • Male
  • Models, Statistical*
  • Numerical Analysis, Computer-Assisted*
  • Sex Factors
  • Skin Diseases / drug therapy
  • Twin Studies as Topic / methods