Handling missing data using multiple imputation in hybrid control clinical trials

Music City Center 

Randomized Controlled Trials (RCTs) are the gold standard in clinical trials. If Historical Data (HD) on the standard of care is available, hybrid control design 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. HD was dynamically borrowed based on outcome similarity: improved estimation accuracy with reduced bias when outcomes were similar, while reasonably controlling the type 1 error when outcomes were dissimilar.

hybrid control

modified power prior