Modeling Recovery Curves With Application to Prostatectomy

TL;DR

This paper introduces a Bayesian model that predicts personalized recovery trajectories for prostatectomy patients, aiding pre-treatment decisions with interpretable and accurate forecasts based on pre-surgery data.

Contribution

The paper presents a novel Bayesian approach for modeling recovery curves, specifically applied to prostatectomy, enhancing personalized medical decision-making.

Findings

The model accurately predicts sexual function recovery trajectories.

Covariate relationships align with existing medical literature.

The approach provides interpretable and personalized predictions.

Abstract

We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. We uncover covariate relationships that agree with and supplement that in existing medical literature.