Meta‐analysis of ordinal outcomes using individual patient data

Meta-analyses are being undertaken in an increasing diversity of diseases and conditions, some of which involve outcomes measured on an ordered categorical scale. We consider methodology for undertaking a meta-analysis on individual patient data for an ordinal response. The approach is based on the proportional odds model, in which the treatment effect is represented by the log-odds ratio. A general framework is proposed for fixed and random effect models. Tests of the validity of the various assumptions made in the meta-analysis models, such as a global test of the assumption of proportional odds between treatments, are presented. The combination of studies with different definitions or numbers of response categories is discussed. The methods are illustrated on two data sets, in a classical framework using SAS and MLn and in a Bayesian framework using BUGS. The relative merits of the three software packages for such meta-analyses are discussed.

[1]  D. Hedeker,et al.  MIXOR: a computer program for mixed-effects ordinal regression analysis. , 1996, Computer methods and programs in biomedicine.

[2]  P. McCullagh.  Regression Models for Ordinal Data , 1980 .

[3]  Kelvyn Jones.  Review of HLM 4 for Windows , 1996 .

[4]  S. Sharp,et al.  Explaining heterogeneity in meta-analysis: a comparison of methods. , 1999 .

[5]  N. Breslow,et al.  Approximate inference in generalized linear mixed models , 1993 .

[6]  B. Carlin,et al.  Bayesian Model Choice Via Markov Chain Monte Carlo Methods , 1995 .

[7]  J. Whitehead.  Sample size calculations for ordered categorical data. , 1993, Statistics in medicine.

[8]  John Geweke,et al.  Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments , 1991 .

[9]  A Whitehead,et al.  A meta-analysis of clinical trials involving different classifications of response into ordered categories. , 1994, Statistics in medicine.

[10]  A Whitehead,et al.  Cholinesterase inhibition for Alzheimer disease: a meta-analysis of the tacrine trials. Dementia Trialists' Collaboration. , 1998, JAMA.

[11]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[12]  H Goldstein,et al.  A multilevel model framework for meta-analysis of clinical trials with binary outcomes. , 2000, Statistics in medicine.

[13]  Andrew Gelman,et al.  General methods for monitoring convergence of iterative simulations , 1998 .

[14]  A Whitehead,et al.  A general parametric approach to the meta-analysis of randomized clinical trials. , 1991, Statistics in medicine.

[15]  H. Goldstein.  Nonlinear multilevel models, with an application to discrete response data , 1991 .

[16]  A Whitehead,et al.  Borrowing strength from external trials in a meta-analysis. , 1996, Statistics in medicine.

[17]  D. Spiegelhalter,et al.  Bayesian Analysis of Realistically Complex Models , 1996 .

[18]  Rory A. Fisher,et al.  Theory of Statistical Estimation , 1925, Mathematical Proceedings of the Cambridge Philosophical Society.

[19]  D. Hedeker,et al.  A random-effects ordinal regression model for multilevel analysis. , 1994, Biometrics.

[20]  John Whitehead,et al.  P106 Analysis of ordered categorical data: Towards a stratified model , 1997 .