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

Stat Med. 2010 May 30;29(12):1340-56. 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.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Bias
  • Biostatistics
  • Logistic Models
  • Markov Chains
  • Meta-Analysis as Topic*
  • Models, Statistical
  • Monte Carlo Method
  • Multivariate Analysis
  • Randomized Controlled Trials as Topic / statistics & numerical data*
  • Stroke / therapy