ArticleBiometrics2018
Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules.
Article in Biometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Participatory-informed preference optimization (PiPrO): A reinforcement learning simulation study.PLOS digital health · 2026Article
- Learning optimal early decision treatment rules with multi-domain intermediate outcomes.Biometrics · 2026Article
- Synergistic self-learning approach to establishing individualized treatment rules from multiple benefit outcomes in a calcium supplementation trial.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Hierarchical taxonomy of psychopathology and personalized mental health treatment selection.Frontiers in psychiatry · 2025Review
- A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.BMC medical research methodology · 2024Article
- Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes.medRxiv : the preprint server for health sciences · 2024Article
- Controlling Cumulative Adverse Risk in Learning Optimal Dynamic Treatment Regimens.Journal of the American Statistical Association · 2024Article
- Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls.Journal of machine learning research : JMLR · 2024Article
- Optimal Personalized Treatment Selection with Multivariate Outcome Measures in a Multiple Treatment Case.Communications in statistics: Simulation and computation · 2023Article
- Reinforced risk prediction with budget constraint using irregularly measured data from electronic health records.Journal of the American Statistical Association · 2023Article
- When the Ends do not Justify the Means: Learning Who is Predicted to Have Harmful Indirect Effects.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- Quantiles based personalized treatment selection for multivariate outcomes and multiple treatments.Statistics in medicine · 2022Article
- The Application of Preference Elicitation Methods in Clinical Trial Design to Quantify Trade-Offs: A Scoping Review.The patient · 2022Article
- Individualized treatment rules under stochastic treatment cost constraints.Journal of causal inference · 2022Article
- Multi-Response Based Personalized Treatment Selection with Data from Crossover Designs for Multiple Treatments.Communications in statistics: Simulation and computation · 2022Article
- Robust Q-learning.Journal of the American Statistical Association · 2021Article
- Learning Individualized Treatment Rules for Multiple-Domain Latent Outcomes.Journal of the American Statistical Association · 2021Article
- Estimation and Optimization of Composite Outcomes.Journal of machine learning research : JMLR · 2021Article
- Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia.JAMA network open · 2020Article
- Precision Medicine.Annual review of statistics and its application · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.