Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/45823
Title: Optimal treatment regime estimation in practice: challenges and choices in a randomized clinical trial for depression
Authors: Stijven, Florian
Tran , Trung Dung
Driessen, Ellen
Abad, Ariel Alonso
MOLENBERGHS, Geert 
VERBEKE, Geert 
Van Mechelen, Iven
Issue Date: 2025
Publisher: OXFORD UNIV PRESS
Source: Biometrics, 81 (1) (Art N° ujaf026)
Abstract: An important aspect of precision medicine is the tailoring of treatments to specific patient types. Nowadays, various methods are available to estimate for this purpose so-called optimal treatment regimes, that is, decision rules for treatment assignment that map patterns of pretreatment characteristics to treatment alternatives and that maximize the expected patient benefit. However, the application of these methods to real-life data has been limited and comes with nonstandard statistical issues. In search of best practices, we reanalyzed data from a randomized clinical trial for the treatment of dysthymic disorder. While the original objective of this trial was to detect a marginally best treatment alternative, we wanted to estimate an optimal treatment regime using 2 prominent estimation methods: Q-learning and value search estimation. An important obstacle in the dataset under study was the occurrence of missing values. This was handled with multiple imputation, a thoughtful implementation of which, however, implied several challenges. Other challenges were implied by the concrete implementation of value search estimation. In this paper, all the choices we have made in the analysis to handle the aforementioned issues are detailed together with a motivation and a description of possible alternatives. Accordingly, this paper may serve as a guide to apply optimal treatment regime estimation in data-analytic practice.
Notes: Stijven, F (corresponding author), Katholieke Univ Leuven, I BioStat, B-3000 Leuven, Belgium.
florian.stijven@kuleuven.be
Keywords: multiple imputation;optimal treatment regimes;Q-learning;randomized clinical trial;value search estimation
Document URI: http://hdl.handle.net/1942/45823
ISSN: 0006-341X
e-ISSN: 1541-0420
DOI: 10.1093/biomtc/ujaf026
ISI #: 001447665700001
Rights: The Author(s) 2025. Published by Oxford University Press on behalf of The International Biometric Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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