Estimation of the optimal regime in treatment of prostate cancer recurrence from observational data using flexible weighting models.

Prostate cancer patients are closely followed after the initial therapy and salvage treatment may be prescribed to prevent or delay cancer recurrence. The salvage treatment decision is usually made dynamically based on the patient's evolving history of disease status and other time-dependent clinical covariates. A multi-center prostate cancer observational study has provided us data on longitudinal prostate specific antigen (PSA) measurements, time-varying salvage treatment, and cancer recurrence time. These data enable us to estimate the best dynamic regime of salvage treatment, while accounting for the complicated confounding of time-varying covariates present in the data. A Random Forest based method is used to model the probability of regime adherence and inverse probability weights are used to account for the complexity of selection bias in regime adherence. The optimal regime is then identified by the largest restricted mean survival time. We conduct simulation studies with different PSA trends to mimic both simple and complex regime adherence mechanisms. The proposed method can efficiently accommodate complex and possibly unknown adherence mechanisms, and it is robust to cases where the proportional hazards assumption is violated. We apply the method to data collected from the observational study and estimate the best salvage treatment regime in managing the risk of prostate cancer recurrence.