Estimating Bayesian Optimal Treatment Regimes for Dichotomous Outcomes using Observational Data
This work addresses the problem of optimizing individualized medical treatments for patients with dichotomous outcomes, such as cancer survival, by incorporating treatment burden considerations, though it is incremental as it builds on existing Bayesian and optimal treatment regime frameworks.
The authors tackled the problem of estimating optimal treatment regimes for dichotomous outcomes in observational data, such as oropharynx cancer treatment, by proposing a Bayesian approach that balances survival probability with treatment burden. Their method reduced chemotherapy assignment frequency by about 75% without lowering average survival probability, offering a strong increase in expected patient quality of life.
Optimal treatment regimes (OTR) are individualised treatment assignment strategies that identify a medical treatment as optimal given all background information available on the individual. We discuss Bayes optimal treatment regimes estimated using a loss function defined on the bivariate distribution of dichotomous potential outcomes. The proposed approach allows considering more general objectives for the OTR than maximization of an expected outcome (e.g., survival probability) by taking into account, for example, unnecessary treatment burden. As a motivating example we consider the case of oropharynx cancer treatment where unnecessary burden due to chemotherapy is to be avoided while maximizing survival chances. Assuming ignorable treatment assignment we describe Bayesian inference about the OTR including a sensitivity analysis on the unobserved partial association of the potential outcomes. We evaluate the methodology by simulations that apply Bayesian parametric and more flexible non-parametric outcome models. The proposed OTR for oropharynx cancer reduces the frequency of the more burdensome chemotherapy assignment by approximately 75% without reducing the average survival probability. This regime thus offers a strong increase in expected quality of life of patients.