ArticleJournal of the American Statistical Association2018
Learning Optimal Personalized Treatment Rules in Consideration of Benefit and Risk: with an Application to Treating Type 2 Diabetes Patients with Insulin Therapies.
Article in Journal of the American Statistical Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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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, 78 citations in OpenAlex.
- Real-Time AI-Assisted Insulin Titration System for Glucose Control in Patients With Type 2 Diabetes: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Association between dynamic dose increases of buprenorphine for treatment of opioid use disorder and risk of relapse.Addiction (Abingdon, England) · 2022Trial
- Optimizing Sequential Decision Rules for Prostate Cancer Biopsy Management: A Multi-Objective Statistical Framework.Journal of the American Statistical Association · 2026Article
- Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments.Biometrics · 2026Article
- Systematic bibliometric and visualized analysis of global research trends, impact, emerging areas, and hotspots of artificial intelligence in personalized medicine.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Constructing a Personalized Treatment Rule for Initial Therapy in Early Parkinson's Disease.Pharmacotherapy · 2026Article
- FEDERATED LEARNING OF ROBUST INDIVIDUALIZED DECISION RULES WITH APPLICATION TO HETEROGENEOUS MULTIHOSPITAL SEPSIS POPULATION.The annals of applied statistics · 2025Article
- 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
- Optimizing warfarin dosing for patients with atrial fibrillation using machine learning.Scientific reports · 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
- Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.Cell reports. Medicine · 2023Review
- Targeted Search for Individualized Clinical Decision Rules to Optimize Clinical Outcomes.Statistics in biosciences · 2022Article
- CAPITAL: Optimal subgroup identification via constrained policy tree search.Statistics in medicine · 2022Article
- Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency.International journal of methods in psychiatric research · 2022Observational
- Estimating the optimal individualized treatment rule from a cost-effectiveness perspective.Biometrics · 2022Article
- Risk controlled decision trees and random forests for precision Medicine.Statistics in medicine · 2022Article
- Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study.BMC medical informatics and decision making · 2022Article
- Estimating optimal dynamic treatment strategies under resource constraints using dynamic marginal structural models.Statistics in medicine · 2021Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
Individualized medical decision making is often complex due to patient treatment response heterogeneity. Pharmacotherapy may exhibit distinct efficacy and safety profiles for different patient populations. An "optimal" treatment that maximizes clinical benefit for a patient may also lead to concern of safety due to a high risk of adverse events. Thus, to guide individualized clinical decision making and deliver optimal tailored treatments, maximizing clinical benefit should be considered in the context of controlling for potential risk. In this work, we propose two approaches to identify personalized optimal treatment strategy that maximizes clinical benefit under a constraint on the average risk. We derive the theoretical optimal treatment rule under the risk constraint and draw an analogy to the Neyman-Pearson lemma to prove the theorem. We present algorithms that can be easily implemented by any off-the-shelf quadratic programming package. We conduct extensive simulation studies to show satisfactory risk control when maximizing the clinical benefit. Lastly, we apply our method to a randomized trial of type 2 diabetes patients to guide optimal utilization of the first line insulin treatments based on individual patient characteristics while controlling for the rate of hypoglycemia events. We identify baseline glycated hemoglobin level, body mass index, and fasting blood glucose as three key factors among 18 biomarkers to differentiate treatment assignments, and demonstrate a successful control of the risk of hypoglycemia in both the training and testing data set.
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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.