Using Joint Models for Longitudinal and Time-to-Event Data to Investigate the Causal Effect of Salvage Therapy after Prostatectomy

TL;DR

This paper employs joint models for longitudinal and time-to-event data to assess the causal impact of salvage therapy timing after prostatectomy, addressing confounding factors in observational prostate cancer data.

Contribution

It introduces a novel application of joint modeling to estimate causal effects of salvage therapy, accounting for PSA as a time-varying confounder and intermediate variable.

Findings

Methodology implemented in R package JMbayes2

Demonstrates causal effect estimation using real registry data

Provides insights into optimal timing of salvage therapy

Abstract

Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen (PSA) measurements. When PSA levels rise, salvage therapies are recommended to decrease the risk of metastasis. However, due to the side effects of these therapies and to avoid over-treatment, it is important to understand which patients and when to initiate these salvage therapies. In this work, we use the University of Michigan Prostatectomy registry Data to tackle this question. Due to the observational nature of this data, we face the challenge that PSA is simultaneously a time-varying confounder and an intermediate variable for salvage therapy. We define different causal salvage therapy effects defined conditionally on different specifications of the longitudinal PSA history. We then illustrate how these effects can be estimated using the framework of joint models for longitudinal and time-to-event data. All proposed methodology is implemented in the freely-available R package JMbayes2.