Modeling Long-term Outcomes and Treatment Effects After Androgen Deprivation Therapy for Prostate Cancer

摘要: Analyzing outcomes in long-term cancer survivor studies can be complex. The effects of predictors on the failure process may difficult to assess over longer periods time, as commonly used assumption proportionality hazards holding an extended period is often questionable. In this manuscript, we compare seven different survival models that estimate hazard rate and proportional non-proportional covariates. particular, focus extension multi-resolution (MRH) estimator, combining a hierarchical MRH approach with data-driven pruning algorithm allows for computational efficiency produces robust estimates even times few observed failures. Using data from large-scale randomized prostate clinical trial, examine patterns biochemical time-varying androgen deprivation therapy treatment other We impact modeling strategies smoothness assumptions estimated effect. Our results show benefits diminish possibly implications future protocols.