Statistics in Medicine

Gibbs sampler for the logistic model in the analysis of longitudinal binary data

Journal Article

  • Author(s): Isabelle Albert, Jean‐Philippe Jais
  • Article first published online: 05 Jan 1999
  • DOI: 10.1002/(SICI)1097-0258(19981230)17:24<2905::AID-SIM911>3.0.CO;2-G
  • Read on Online Library
  • Subscribe to Journal


Logistic mixed‐effects models constitute a natural framework to study longitudinal binary response variables when the question addressed with the data is related to covariate effects within persons. However, the computations of the likelihoods are generally tedious and require the resolution of integrals which have no analytical solution. In this paper, we study a logistic mixed‐effects model in a Bayesian framework and use the Gibbs sampler to overcome the current computational limitations. From a study of side‐effects occurring during plasma exchanges, we explore the issues of Bayesian formulation, model parametrization, choice of the prior distributions, diagnosing convergence, comparison between models and model adequacy. Finally, we show that a Bayesian random‐effects model is useful to facilitate prediction. © 1998 John Wiley & Sons, Ltd.

Related Topics

Related Publications

Related Content

Site Footer


This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.