Nonparametric Bayesian approach for dynamic borrowing of historical control data.

Abstract

When incorporating historical control data into the analysis of current randomized controlled trial data, it is critical to account for differences between the datasets. When the cause of difference is an unmeasured factor and adjustment for only observed covariates is insufficient, it is desirable to use a dynamic borrowing method that reduces the impact of heterogeneous historical controls. We propose a nonparametric Bayesian approach that addresses between-trial heterogeneity and allows borrowing historical controls homogeneous with the current control. Additionally, to emphasize conflict resolution between historical controls and the current control, we introduce a method based on the dependent Dirichlet process (DP) mixture. The proposed methods can be implemented using the same procedure, regardless of whether the outcome data comprise aggregated study-level data or individual participant data. We also develop a novel index of similarity between the historical and current control data, based on the posterior distribution of the parameter of interest. We conduct a simulation study and analyze clinical trial examples to evaluate the performance of the proposed methods compared to existing methods. The proposed method, based on the dependent DP mixture, can accurately borrow from homogeneous historical controls while reducing the impact of heterogeneous historical controls compared to the typical DP mixture. The proposed methods outperform existing methods in scenarios with heterogeneous historical controls, in which the meta-analytic approach is ineffective.