On Estimation and Cross-validation of Dynamic Treatment Regimes with Competing Risks

TL;DR

This paper develops methods to evaluate and validate dynamic treatment strategies for renal replacement therapy timing in critically ill AKI patients, using observational ICU data and competing risks models.

Contribution

It introduces two approaches for estimating optimal treatment regimes with competing risks and proposes a cross-validation method for their performance assessment.

Findings

Demonstrates the application of inverse probability weighted Aalen-Johansen estimator.

Shows the effectiveness of dynamic-regime marginal structural models.

Provides a practical cross-validation technique for treatment regime evaluation.

Abstract

The optimal moment to start renal replacement therapy in a patient with acute kidney injury (AKI) remains a challenging problem in intensive care nephrology. Multiple randomised controlled trials have tried to answer this question, but these can, by definition, only analyse a limited number of treatment initiation strategies. In view of this, we use routinely collected observational data from the Ghent University Hospital intensive care units (ICUs) to investigate different pre-specified timing strategies for renal replacement therapy initiation based on time-updated levels of serum potassium, pH and fluid balance in critically ill patients with AKI with the aim to minimize 30-day ICU mortality. For this purpose, we apply statistical techniques for evaluating the impact of specific dynamic treatment regimes in the presence of ICU discharge as a competing event. We discuss two approaches, a non-parametric one - using an inverse probability weighted Aalen-Johansen estimator - and a semiparametric one - using dynamic-regime marginal structural models. Furthermore, we suggest an easy to implement cross-validation technique that can be used for the out-of-sample performance assessment of the optimal dynamic treatment regime. Our work illustrates the potential of data-driven medical decision support based on routinely collected observational data.