ArticleBiometrics2016
A Bayesian credible subgroups approach to identifying patient subgroups with positive treatment effects.
Article in Biometrics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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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
24 citing papers in PubMed.
- Adverse Events and Perception of Benefit From Duloxetine for Treating Aromatase Inhibitor-Associated Arthralgias.JNCI cancer spectrum · 2021Trial
- Bayesian Nonparametric Model for Heterogeneous Treatment Effects With Zero-Inflated Data.Statistics in medicine · 2024Article
- Testing for a treatment effect in a selected subgroup.Statistical methods in medical research · 2024Article
- Uncovering individualised treatment effects for educational trials.Scientific reports · 2024Article
- A pseudo-response approach to constructing confidence intervals for the subset of patients expected to benefit from a new treatment.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Multi-way overlapping clustering by Bayesian tensor decomposition.Statistics and its interface · 2024Article
- Bayesian cluster analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Article
- A Bayesian decision-theoretic design for simultaneous biomarker-based subgroup selection and efficacy evaluation.Statistics in biopharmaceutical research · 2022Article
- Propensity score weighting for causal subgroup analysis.Statistics in medicine · 2021Article
- An Application of the Patient Rule-Induction Method to Detect Clinically Meaningful Subgroups from Failed Phase III Clinical Trials.International journal of clinical biostatistics and biometrics · 2021Article
- Estimation and Optimization of Composite Outcomes.Journal of machine learning research : JMLR · 2021Article
- Evaluation of conditional treatment effects of adjuvant treatments on patients with synovial sarcoma using Bayesian subgroup analysis.BMC medical informatics and decision making · 2020Article
- A critical review of graphics for subgroup analyses in clinical trials.Pharmaceutical statistics · 2020Review
- Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.Journal of biomedical informatics · 2020Article
- Using the Bayesian credible subgroups method to identify populations benefiting from treatment: An application to the Look AHEAD trial.PloS one · 2020Article
- Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis - An Application to the International Diabetes Management Practice Study.Frontiers in artificial intelligence · 2020Article
- Bayesian credible subgroup identification for treatment effectiveness in time-to-event data.PloS one · 2020Article
- Bayesian additive regression trees and the General BART model.Statistics in medicine · 2019Article
- A nonparametric Bayesian basket trial design.Biometrical journal. Biometrische Zeitschrift · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Many new experimental treatments benefit only a subset of the population. Identifying the baseline covariate profiles of patients who benefit from such a treatment, rather than determining whether or not the treatment has a population-level effect, can substantially lessen the risk in undertaking a clinical trial and expose fewer patients to treatments that do not benefit them. The standard analyses for identifying patient subgroups that benefit from an experimental treatment either do not account for multiplicity, or focus on testing for the presence of treatment-covariate interactions rather than the resulting individualized treatment effects. We propose a Bayesian credible subgroups method to identify two bounding subgroups for the benefiting subgroup: one for which it is likely that all members simultaneously have a treatment effect exceeding a specified threshold, and another for which it is likely that no members do. We examine frequentist properties of the credible subgroups method via simulations and illustrate the approach using data from an Alzheimer's disease treatment trial. We conclude with a discussion of the advantages and limitations of this approach to identifying patients for whom the treatment is beneficial.
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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.