Robust Causal Comparison of Nonrandomized Groups in Oncology Studies The goal of our research is to develop robust statistical models for causal comparison of nonrandomized groups. With the increasing availability of real world data (RWD), the trial design to compare a new therapy versus an external control obtained via RWD (e.g., EHR) has received renewed attention recently. This design has been utilized in therapeutic development especially in rare diseases when RCT is not feasible, e.g., comparing 3-year relapse-free survival (RFS) between locally treated high-risk ocular melanoma patients on adjuvant combination immunotherapy versus a matched contemporaneous control obtained outside of the trial. Then the challenge is at making causal inference on whether the treatment is efficacious in prolonging (e.g.) patient survival. But which method? Causal inference is known to depend on various assumptions. Despite advances in making various causal inference methods robust, e.g., most notably the doubly robust estimate (DRE) existing DREs continue to suffer several major drawbacks, e.g., being too sensitive to mild model misspecifications. Our preliminary studies on enhanced DREs that shows the needed robustness in making causal inference realized through semi-parametric models for trials limited with continuous primary endpoint. Built upon this development, the goal of this application is to develop the novel DRE approach for analyzing nonrandomized clinical trials with binary (such as response in oncology, incidence in epidemiology) and time to event (such as survival and progression free survival in oncology) outcomes and assess their statistical properties, which as is well known can be quite different from the proof of principle case with continuous outcome (Aims 1-2). The methods will then be applied to an NCI sponsored trial, and a population science study and one ongoing immunotherapy trial.