Purpose: To assess the ability of a new Bayesian methodological approach to adjust for bias due to confounding when combining evidence from randomised and non-randomised studies.
Method: This study used Bayesian hierarchical modelling to combine evidence from randomised and non-randomised studies and compared alternative approaches in terms of their ability to accommodate imbalances in patient characteristics within studies that could confound the results. In the new methodological approach, study estimates were adjusted for potential confounders using differences in patient characteristics (e.g., age) between study arms. We compared the results of the Bayesian hierarchical model adjusted for differences in study arms with two other Bayesian approaches: 1) results adjusted for aggregate study values and 2) downweighting the potentially biased non-randomised studies. A simulation study was used to examine the ability of the new and alternative models to account for imbalances and to assess the sensitivity of the results to changes in the relative number of studies of each type, the study sizes, the actual magnitude of the bias, and other sources of heterogeneity.
Result: For all scenarios considered, the Bayesian hierarchical model adjusted for differences within studies gave results that were closer to the “truth” compared to the other models.
Conclusion: Covariate adjustment using differences in patient characteristics between study arms provides a systematic way of adjusting for bias due to confounding that is robust to changes in the relative number of studies of each type, the study sizes, the magnitude of the bias, and other sources of heterogeneity. Where informed health care decision making requires the synthesis of evidence from randomised and non-randomised study designs such an approach could facilitate the use of all available evidence.
Candidate for the Lee B. Lusted Student Prize Competition