Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules

Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12743
Publisher site
See Article on Publisher Site

Abstract

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

Journal

BiometricsWiley

Published: Jan 1, 2018

Keywords: ; ; ;

References

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