Evaluating the Heterogeneity of Treatment Effects Across Subgroups with Existance of Missing Data

Music City Center 

In clinical trials, it is important to understand whether the treatment effects are consistent across different subgroups defined based on key baseline factors. However, there is a lack of proper statistical methodology for testing treatment effect heterogeneity in cases where multiple imputation methods are used to handle missing data. Moreover, treatment effect heterogeneity is traditionally tested by adding treatment-by-subgroup interaction to the primary analysis models, but recently published analysis models for improved estimation efficiency can be too complicated to properly add such interaction terms. In this article, we propose a separate model framework to test the heterogeneity of treatment effect across subgroups by constructing a chi-square statistic based on the inferential results from models within each subgroup. Our proposed approach can control the type I error rate well by properly accounting for the correlations introduced during multiple imputation and is applicable to all analysis models. The performance of the proposed method is evaluated using simulations and applies to a real clinical trial.

Subgroup

Separate Model

Rubin’s Rule

Bootstrap 

Biometrics Section