ReviewJCO precision oncology2019
Bayesian Approaches to Subgroup Analysis and Related Adaptive Clinical Trial Designs.
Review in JCO precision oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Exploration of predictors of benefit from nivolumab monotherapy for patients with pretreated advanced gastric and gastroesophageal junction cancer: post hoc subanalysis from the ATTRACTION-2 study.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2022Trial
- Type I error control and interim monitoring for co-primary hypotheses involving a subgroup in the Outpatient Treatment with Anti-Coronavirus Immunoglobulin (OTAC) trial.Contemporary clinical trials communications · 2026Article
- Drug Development in Pediatric Chronic Kidney Disease: A Review of Promising Treatments, Old Challenges, and New Strategies.Paediatric drugs · 2025Review
- An interpretable framework to identify responsive subgroups from clinical trials regarding treatment effects: Application to treatment of intracerebral hemorrhage.PLOS digital health · 2024Article
- A permutation procedure to detect heterogeneous treatment effects in randomized clinical trials while controlling the type I error rate.Clinical trials (London, England) · 2022Article
- A Bayesian decision-theoretic design for simultaneous biomarker-based subgroup selection and efficacy evaluation.Statistics in biopharmaceutical research · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We review Bayesian and Bayesian decision theoretic approaches to subgroup analysis and applications to subgroup-based adaptive clinical trial designs. Subgroup analysis refers to inference about subpopulations with significantly distinct treatment effects. The discussion mainly focuses on inference for a benefiting subpopulation, that is, a characterization of a group of patients who benefit from the treatment under consideration more than the overall population. We introduce alternative approaches and demonstrate them with a small simulation study. Then, we turn to clinical trial designs. When the selection of the interesting subpopulation is carried out as the trial proceeds, the design becomes an adaptive clinical trial design, using subgroup analysis to inform the randomization and assignment of treatments to patients. We briefly review some related designs. There are a variety of approaches to Bayesian subgroup analysis. Practitioners should consider the type of subpopulations in which they are interested and choose their methods accordingly. We demonstrate how subgroup analysis can be carried out by different Bayesian methods and discuss how they identify slightly different subpopulations.
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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.