ArticleBiometrics2015
Using decision lists to construct interpretable and parsimonious treatment regimes.
Article in Biometrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
40 citing papers in PubMed.
- Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study.Journal of diabetes science and technology · 2024Trial
- Culture eats evidence for breakfast: how culture influences implementation of evidence-based practices.Implementation science communications · 2026Article
- Variable importance measures for heterogeneous treatment effects.Biometrics · 2025Article
- Simultaneous Feature Selection for Optimal Dynamic Treatment Regimens.Statistics in medicine · 2025Article
- Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data.Journal of machine learning research : JMLR · 2025Article
- Electronic Clinical Decision Support Tools: Strategies to Improve the Management of Lower Respiratory Tract Infections in Low-Resource Settings.The American journal of tropical medicine and hygiene · 2024Article
- Machine Learning and Health Science Research: Tutorial.Journal of medical Internet research · 2024Article
- Transfer learning of individualized treatment rules from experimental to real-world data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design.CEUR workshop proceedings · 2022Article
- GENERALIZATION ERROR BOUNDS OF DYNAMIC TREATMENT REGIMES IN PENALIZED REGRESSION-BASED LEARNING.Annals of statistics · 2022Article
- Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study.BMC medical informatics and decision making · 2022Article
- Focusing on optimality for the translation of precision medicine.Journal of clinical and translational science · 2022Article
- Meta-analysis of individualized treatment rules via sign-coherency.Proceedings of machine learning research · 2022Article
- Conducting Real-world Evidence Studies on the Clinical Outcomes of Diabetes Treatments.Endocrine reviews · 2021Article
- High-Dimensional Precision Medicine From Patient-Derived Xenografts.Journal of the American Statistical Association · 2021Article
- A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity.Journal of the American Statistical Association · 2021Article
- Evaluation of a Stepped-Care eHealth HIV Prevention Program for Diverse Adolescent Men Who Have Sex With Men: Protocol for a Hybrid Type 1 Effectiveness Implementation Trial of SMART.JMIR research protocols · 2020Article
- Identifying optimal level-of-care placement decisions for adolescent substance use treatment.Drug and alcohol dependence · 2020Observational
- Assessing the incremental value of new biomarkers based on OR rules.Biostatistics (Oxford, England) · 2020Article
- Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome.Statistical methods in medical research · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
A treatment regime formalizes personalized medicine as a function from individual patient characteristics to a recommended treatment. A high-quality treatment regime can improve patient outcomes while reducing cost, resource consumption, and treatment burden. Thus, there is tremendous interest in estimating treatment regimes from observational and randomized studies. However, the development of treatment regimes for application in clinical practice requires the long-term, joint effort of statisticians and clinical scientists. In this collaborative process, the statistician must integrate clinical science into the statistical models underlying a treatment regime and the clinician must scrutinize the estimated treatment regime for scientific validity. To facilitate meaningful information exchange, it is important that estimated treatment regimes be interpretable in a subject-matter context. We propose a simple, yet flexible class of treatment regimes whose members are representable as a short list of if-then statements. Regimes in this class are immediately interpretable and are therefore an appealing choice for broad application in practice. We derive a robust estimator of the optimal regime within this class and demonstrate its finite sample performance using simulation experiments. The proposed method is illustrated with data from two clinical trials.
Indexed as
Identifiers
What OpenQuestion holds
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.