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Meta-analysis of ordinal outcomes using individual patient data
A Whitehead1, R Z Omar, J P Higgins
1Medical and Pharmaceutical Statistics Research Unit, The University of Reading, P.O. Box 240, Earley Gate, Reading RG6 6FN, U.K. p.a.whitehead@reading.ac.uk
Statistics in Medicine
|July 27, 2001
Summary
This study presents a new meta-analysis method for individual patient data with ordered categorical outcomes. The proportional odds model framework is detailed, offering robust analysis for diverse diseases.
Area of Science:
- Biostatistics
- Epidemiology
- Medical Research Methodology
Background:
- Meta-analyses are increasingly common across various diseases.
- Many studies involve outcomes measured on an ordered categorical scale.
- Existing meta-analysis methods may not fully accommodate ordinal response data.
Purpose of the Study:
- To propose a methodology for meta-analysis of individual patient data (IPD) with ordinal outcomes.
- To provide a general framework for fixed and random effect models for ordinal data.
- To discuss methods for handling heterogeneity in response categories across studies.
Main Methods:
- The approach is based on the proportional odds model, using the log-odds ratio to represent treatment effects.
- A general framework for both fixed and random effect models is proposed.
- Tests for model assumptions, including the proportional odds assumption, are presented.
Main Results:
- The proposed methodology allows for meta-analysis of IPD with ordinal outcomes.
- The framework accommodates both fixed and random effects.
- Methods for combining studies with varying response categories are discussed.
Conclusions:
- The proportional odds model provides a suitable framework for meta-analysis of ordinal IPD.
- The proposed methods offer flexibility in handling different study designs and outcome definitions.
- The study illustrates practical application using SAS, MLn, and BUGS software.
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