Differentially Private Generative Adversarial Networks with Model Inversion

Can a propensity score matching method be applied to assessing efficacy from single-arm proof-of-concept trials in oncology?

As a result of the escalating number of new cancer treatments being developed and competition among pharmaceutical companies, decisions regarding how to proceed with phase III trials are frequently based on findings from either single-arm phase I expansion cohorts or phase II studies that compare the efficacy of the study drug to a standard-of-care benchmark derived from historical data. However, even when eligibility criteria are matched, differences in the distribution of baseline patient features may influence the outcome of single-arm trials in real-world scenarios. Therefore, novel methods are needed to enhance the accuracy of efficacy prediction from current cohorts relative to historical data. In this study, we demonstrated the feasibility of using the propensity score matching (PSM) method to improve decision making by matching relevant baseline features between current and historical cohorts. According to our findings, utilizing the PSM method may provide a less biased means of comparing outcomes between current and historical cohorts relative to a naïve approach, which relies solely on differences in average outcomes between the cohorts.