IntroductionIt is widely recognized that best possible clinical care is tailored to individual patient characteristics (Sox et al.,; Hamburg and Collins, ; Collins and Varmus, ), including subjective factors like patient personal preference (Edwards and Elwyn, ). Individualized treatment rules (ITRs) formalize personalized clinical care as a function from up‐to‐date patient information to a recommended treatment. An optimal ITR maximizes the mean of some pre‐specified clinical outcome if applied to make treatment decisions for all patients in a population of interest. This definition of optimality is mathematically convenient as it reduces estimation of an optimal ITR to a scalar optimization problem over a class of potential ITRs. However, this formulation does not directly allow for shared decision making wherein patient preferences are integrated into the decision process (Drake et al., ; Barry and Edgman‐Levitan, ); on the other hand, direct preference elicitation in which the patient chooses parameters indexing a composite outcome is not feasible unless patients have undergone specialized training (Brennan, ; Braziunas, ; Lizotte et al., ). Thus, a common approach for preference elicitation is to administer a questionnaire populated with items that are accessible (meaningful) to a patient in a domain context yet are informative about preferences
Biometrics – Wiley
Published: Jan 1, 2018
Keywords: ; ; ;
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