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The optimal moment to start renal replacement therapy in a patient with acutekidney injury (AKI) remains a challenging problem in intensive care nephrology.Multiple randomised controlled trials have tried to answer this question, butthese can, by definition, only analyse a limited number of treatment initiationstrategies. In view of this, we use routinely collected observational data fromthe Ghent University Hospital intensive care units (ICUs) to investigatedifferent pre-specified timing strategies for renal replacement therapyinitiation based on time-updated levels of serum potassium, pH and fluidbalance in critically ill patients with AKI with the aim to minimize 30-day ICUmortality. For this purpose, we apply statistical techniques for evaluating theimpact of specific dynamic treatment regimes in the presence of ICU dischargeas a competing event. We discuss two approaches, a non-parametric one - usingan inverse probability weighted Aalen-Johansen estimator - and a semiparametricone - using dynamic-regime marginal structural models. Furthermore, we suggestan easy to implement cross-validation technique that can be used for theout-of-sample performance assessment of the optimal dynamic treatment regime.Our work illustrates the potential of data-driven medical decision supportbased on routinely collected observational data.

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