Controlling ecological bias in evidence synthesis of trials reporting on collapsed and overlapping covariate categories

Govan, L. , Ades, A. E., Weir, C. J., Welton, N. J. and Langhorne, P. (2010) Controlling ecological bias in evidence synthesis of trials reporting on collapsed and overlapping covariate categories. Statistics in Medicine, 29(12), pp. 1340-1356. (doi: 10.1002/sim.3869)

Abstract

Meta-analysis of randomized controlled trials based on aggregated data is vulnerable to ecological bias if trial results are pooled over covariates that influence the outcome variable, even when the covariate does not modify the treatment effect, or is not associated with the treatment. This paper shows how, when trial results are aggregated over different levels of covariates, the within-study covariate distribution, and the effects of both covariates and treatments can be simultaneously estimated, and ecological bias reduced. Bayesian Markov chain Monte Carlo methods are used. The method is applied to a mixed treatment comparison evidence synthesis of six alternative approaches to post-stroke inpatient care. Results are compared with a model using only the stratified covariate data available, where each stratum is treated as a separate trial, and a model using fully aggregated data, where no covariate data are used.

• Item Type: Articles
• Keywords: BAYESIAN SYNTHESIS BIOLOGY CARE CHAIN CONTROLLED-TRIAL Distribution ecologic bias ENGLAND Epidemiology evidence synthesis HEALTH IMMUNODEFICIENCY-VIRUS PREVALENCE INDIVIDUAL-LEVEL LEVEL LEVEL DATA meta-analysis METAANALYSIS MIXED TREATMENT COMPARISONS MODEL outcome PATIENT-LEVEL POLICY randomized controlled trials REGRESSION Scotland SURVEILLANCE DATA Treatment TRIAL trials
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Govan, Dr Lindsay and Langhorne, Professor Peter
• Authors: Govan, L., Ades, A. E., Weir, C. J., Welton, N. J., and Langhorne, P.
• College/School: College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health; College of Medical Veterinary and Life Sciences; College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Health Economics and Health Technology Assessment
• Journal Name: Statistics in Medicine
• ISSN: 0277-6715