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Meta‐analysis of ordinal outcomes using individual patient data
Author(s) -
Whitehead Anne,
Omar Rumana Z.,
Higgins Julian P. T.,
Savaluny Elly,
Turner Rebecca M.,
Thompson Simon G.
Publication year - 2001
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.919
Subject(s) - categorical variable , odds , ordinal data , meta analysis , random effects model , bayesian probability , ordinal regression , computer science , ordinal scale , odds ratio , statistics , econometrics , mathematics , medicine , logistic regression
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. Copyright © 2001 John Wiley & Sons, Ltd.