A Comprehensive Framework for the Evaluation of Individual Treatment Rules From Observational Data

TL;DR

This paper introduces a new probabilistic framework and estimation methods, including an EM algorithm, for evaluating individualized treatment rules from observational data, addressing challenges of implementation and causal inference.

Contribution

It presents a novel probabilistic model and estimation procedures, including an EM algorithm, for assessing ITRs in observational studies with partially implemented rules.

Findings

Estimators are unbiased with correct confidence interval coverage.

The framework effectively evaluates ITRs using observational data.

Application to MIMIC-III data demonstrates practical utility.

Abstract

Individualized treatment rules (ITRs) are deterministic decision rules that recommend treatments to individuals based on their characteristics. Though ubiquitous in medicine, ITRs are hardly ever evaluated in randomized controlled trials. To evaluate ITRs from observational data, we introduce a new probabilistic model and distinguish two situations: i) the situation of a newly developed ITR, where data are from a population where no patient implements the ITR, and ii) the situation of a partially implemented ITR, where data are from a population where the ITR is implemented in some unidentified patients. In the former situation, we propose a procedure to explore the impact of an ITR under various implementation schemes. In the latter situation, on top of the fundamental problem of causal inference, we need to handle an additional latent variable denoting implementation. To evaluate ITRs in this situation, we propose an estimation procedure that relies on an expectation-maximization algorithm. In Monte Carlo simulations our estimators appear unbiased with confidence intervals achieving nominal coverage. We illustrate our approach on the MIMIC-III database, focusing on ITRs for dialysis initiation in patients with acute kidney injury.