Handing Missing Data Using Multiple Imputation in Hybrid Control Clinical Trials with Modified Power Prior

Randomized Controlled Trials (RCTs) are the gold standard in clinical trials. If Historical Data (HD) on the standard of care is available, hybrid control clinical trials can provide more evidence than a standalone RCT with unequal allocation. HD for the control group can often be derived from real-world data, which frequently includes missing covariates data. However, such missingness may introduce bias depending on missing data mechanisms and analytical methods. In this study, we propose addressing covariate missingness under the missing at random assumption by multiple imputation. In the analysis stage, we utilize a combination of propensity score matching and modified power prior. The simulation showed that complete case analysis caused bias under outcome and covariate-dependent covariate missingness, while multiple imputation provided nearly unbiased estimates and improved precision when HD was similar to the current trial data under the missing at random assumption. HD was dynamically borrowed based on outcome similarity: improved estimation accuracy with reduced bias and increased power when outcomes were similar, while reasonably controlling the type I error when outcomes were dissimilar. The proposed method was also applied to a real clinical trial data to illustrate its practical performance. These results suggest that the approach may be useful in hybrid control clinical trials that utilize real world data with missing covariates.