ArticleBiometrics2014
Set-valued dynamic treatment regimes for competing outcomes.
Article in Biometrics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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.
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Who cites it
32 citing papers in PubMed.
- Optimizing Sequential Decision Rules for Prostate Cancer Biopsy Management: A Multi-Objective Statistical Framework.Journal of the American Statistical Association · 2026Article
- A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.BMC medical research methodology · 2024Article
- Fusing Individualized Treatment Rules Using Secondary Outcomes.Proceedings of machine learning research · 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
- 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
- Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments.Journal of machine learning research : JMLR · 2023Article
- Targeted Search for Individualized Clinical Decision Rules to Optimize Clinical Outcomes.Statistics in biosciences · 2022Article
- Quantiles based personalized treatment selection for multivariate outcomes and multiple treatments.Statistics in medicine · 2022Article
- Risk controlled decision trees and random forests for precision Medicine.Statistics in medicine · 2022Article
- Multi-Response Based Personalized Treatment Selection with Data from Crossover Designs for Multiple Treatments.Communications in statistics: Simulation and computation · 2022Article
- Step-adjusted tree-based reinforcement learning for evaluating nested dynamic treatment regimes using test-and-treat observational data.Statistics in medicine · 2021Article
- Evaluating the Effectiveness of Personalized Medicine With Software.Frontiers in big data · 2021Article
- Estimation and Optimization of Composite Outcomes.Journal of machine learning research : JMLR · 2021Article
- Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness.Statistics in medicine · 2020Article
- Learning-based biomarker-assisted rules for optimized clinical benefit under a risk constraint.Biometrics · 2020Article
- Near-optimal Individualized Treatment Recommendations.Journal of machine learning research : JMLR · 2020Article
- Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals.Journal of the American Statistical Association · 2020Article
- Precision Medicine.Annual review of statistics and its application · 2019Article
- Identifying optimal dosage regimes under safety constraints: An application to long term opioid treatment of chronic pain.Statistics in medicine · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Dynamic treatment regimes (DTRs) operationalize the clinical decision process as a sequence of functions, one for each clinical decision, where each function maps up-to-date patient information to a single recommended treatment. Current methods for estimating optimal DTRs, for example Q-learning, require the specification of a single outcome by which the "goodness" of competing dynamic treatment regimes is measured. However, this is an over-simplification of the goal of clinical decision making, which aims to balance several potentially competing outcomes, for example, symptom relief and side-effect burden. When there are competing outcomes and patients do not know or cannot communicate their preferences, formation of a single composite outcome that correctly balances the competing outcomes is not possible. This problem also occurs when patient preferences evolve over time. We propose a method for constructing DTRs that accommodates competing outcomes by recommending sets of treatments at each decision point. Formally, we construct a sequence of set-valued functions that take as input up-to-date patient information and give as output a recommended subset of the possible treatments. For a given patient history, the recommended set of treatments contains all treatments that produce non-inferior outcome vectors. Constructing these set-valued functions requires solving a non-trivial enumeration problem. We offer an exact enumeration algorithm by recasting the problem as a linear mixed integer program. The proposed methods are illustrated using data from the CATIE schizophrenia study.
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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.