C-learning in estimation of optimal individualized treatment regimes for recurrent disease

TL;DR

This paper introduces Recurrent C-learning (ReCL), a novel method for estimating optimal individualized treatment regimes specifically designed for recurrent events, such as cancer recurrences, using classification techniques to improve treatment decision-making.

Contribution

The paper develops ReCL, a new approach that reformulates the ITR estimation for recurrent events into a weighted classification problem, extending beyond existing methods focused on first-time events.

Findings

ReCL effectively identifies optimal treatment regimes in simulations.

ReCL provides interpretable treatment rules for colorectal cancer data.

The method outperforms traditional approaches in recurrent event scenarios.

Abstract

Recurrent events, characterized by the repeated occurrence of the same event in an individual, are a common type of data in medical research. Motivated by cancer recurrences, we aim to estimate the optimal individualized treatment regime (ITR) that effectively mitigates such recurrent events. An ITR is a decision rule that assigns the optimal treatment to each patient, based on personalized information, with the aim of maximizing the overall therapeutic benefits. However, existing studies of estimating ITR mainly focus on first-time events rather than recurrent events. To address the issue of determining the optimal ITR for recurrent events, we propose the Recurrent C-learning (ReCL) method to identify the optimal ITR from two or multiple treatment options. The proposed method reformulates the optimization problem into a weighted classification problem. We introduce three estimators for the misclassification cost: the outcome regression estimator, the inverse probability weighting estimator, and the augmented inverse probability weighting estimator. The ReCL method leverages classification techniques to generate an interpretable optimal ITR tailored for recurrent event data. The advantages of the ReCL method are demonstrated through simulations under various scenarios. Furthermore, based on real data on colorectal cancer treatments, we employ this novel method to derive interpretable tree treatment regimes for colorectal cancer, thus providing a practical framework for enhancing treatment strategies.