Jeremy M G Taylor, of the Department of Biostatistics at the University of Michigan, will present:

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

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 in order 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 for 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. Specifically, for each patient, at each time they could receive salvage therapy, we define the causal effect for that patient as the difference in the probability of developing metastases within 2 years if they were to receive salvage therapy compared to not receiving salvage therapy. These effects are averaged over appropriate subsets of the patients to a marginal causal effect of salvage therapy. We then illustrate how these effects can be estimated using a Bayesian approach within the framework of joint models for longitudinal and time-to-event data.

This is joint work with Dimitris Rizopoulos and Grigorios Papageorgiou from Erasmus University Medical Center

A seminar tea will be held at 11:00 a.m. in University Office Plaza, Room 240. All are Welcome.