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

TL;DR

This study compares seven survival models, including an advanced multi-resolution hazard approach, to analyze long-term treatment effects and failure patterns in prostate cancer patients, revealing that treatment benefits decrease over time.

Contribution

It introduces a non-proportional hierarchical MRH model with a data-driven pruning algorithm for robust, efficient long-term survival analysis in prostate cancer.

Findings

Treatment benefits diminish over time.

The proposed model provides robust estimates even with few failures.

Different modeling strategies affect estimated treatment effects.

Abstract

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