|Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules.
|Year of Publication
|Butler, Emily L., Eric B. Laber, Sonia M. Davis, and Michael R. Kosorok
|Antipsychotic Agents, Humans, Models, Statistical, Patient Preference, Precision Medicine, Schizophrenia, Surveys and Questionnaires, Treatment Outcome
Precision medicine seeks to provide treatment only if, when, to whom, and at the dose it is needed. Thus, precision medicine is a vehicle by which healthcare can be made both more effective and efficient. Individualized treatment rules operationalize precision medicine as a map from current patient information to a recommended treatment. An optimal individualized treatment rule is defined as maximizing the mean of a pre-specified scalar outcome. However, in settings with multiple outcomes, choosing a scalar composite outcome by which to define optimality is difficult. Furthermore, when there is heterogeneity across patient preferences for these outcomes, it may not be possible to construct a single composite outcome that leads to high-quality treatment recommendations for all patients. We simultaneously estimate the optimal individualized treatment rule for all composite outcomes representable as a convex combination of the (suitably transformed) outcomes. For each patient, we use a preference elicitation questionnaire and item response theory to derive the posterior distribution over preferences for these composite outcomes and subsequently derive an estimator of an optimal individualized treatment rule tailored to patient preferences. We prove that as the number of subjects and items on the questionnaire diverge, our estimator is consistent for an oracle optimal individualized treatment rule wherein each patient's preference is known a priori. We illustrate the proposed method using data from a clinical trial on antipsychotic medications for schizophrenia.
|Incorporating patient preferences into estimation of optimal individualized treatment rules.
|PubMed Central ID
|P01 CA142538 / CA / NCI NIH HHS / United States
R01 DK108073 / DK / NIDDK NIH HHS / United States