Missing data in the eligibility criteria of synthetic controls from real-world data

Randomized clinical trials (RCTs) can benefit from using Real-World Data (RWD) as a supplementary data source to enhance their analysis. An Augmented RCT combines randomized treatment and control groups with synthetic controls derived from RWD. This way, the trial can achieve less prospective enrollment, higher statistical power, and lower costs. However, to ensure scientific validity, the synthetic controls must satisfy the same eligibility criteria as the trial participants. A major challenge is that RWD often have missing data that hinder the eligibility assessment. This problem has been overlooked in the literature and this paper offers statistical solutions to address it. We use multiple imputations to handle missing data in the variables involved in the eligibility criteria. We also propose a generalized propensity score weighting procedure to adjust for the life expectancy requirement, a common eligibility criterion in oncology clinical trials but usually unavailable in RWD. Since the life expectancy is an unmeasured confounder, we discuss the statistical assumptions required to correct its bias. We validate the proposed solutions through simulation studies and the analysis of an Augmented RCT in oncology.