ArticleStatistical methods in medical research2021
Statistical design considerations for trials that study multiple indications.
Article in Statistical methods in medical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 0 citations in OpenAlex.
- Bayesian local exchangeability design for phase II basket trials.Statistics in medicine · 2022Trial
- A group-sequential randomized trial design utilizing supplemental trial data.Statistics in medicine · 2022Trial
- Incorporating historic information to further improve power when conducting Bayesian information borrowing in basket trials.Biostatistics (Oxford, England) · 2025Article
- Designing trials with multiple objectives with graphical approach for multiple scenarios.Contemporary clinical trials communications · 2025Article
- Master Protocol Design With Hybrid Control for Efficient Early-Phase Trial Consolidation.JCO precision oncology · 2025Article
- Leveraging information from secondary endpoints to enhance dynamic borrowing across subpopulations.Biometrics · 2024Article
- New clinical trial design borrowing information across patient subgroups based on fusion-penalized regression models.Statistical methods in medical research · 2024Article
- Recent innovations in adaptive trial designs: A review of design opportunities in translational research.Journal of clinical and translational science · 2023Review
- Guidance on interim analysis methods in clinical trials.Journal of clinical and translational science · 2023Review
- Basket Trials: Review of Current Practice and Innovations for Future Trials.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2022Review
- Article
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 at 4 institutions in 1 country.
Funding
Abstract
Breakthroughs in cancer biology have defined new research programs emphasizing the development of therapies that target specific pathways in tumor cells. Innovations in clinical trial design have followed with master protocols defined by inclusive eligibility criteria and evaluations of multiple therapies and/or histologies. Consequently, characterization of subpopulation heterogeneity has become central to the formulation and selection of a study design. However, this transition to master protocols has led to challenges in identifying the optimal trial design and proper calibration of hyperparameters. We often evaluate a range of null and alternative scenarios; however, there has been little guidance on how to synthesize the potentially disparate recommendations for what may be optimal. This may lead to the selection of suboptimal designs and statistical methods that do not fully accommodate the subpopulation heterogeneity. This article proposes novel optimization criteria for calibrating and evaluating candidate statistical designs of master protocols in the presence of the potential for treatment effect heterogeneity among enrolled patient subpopulations. The framework is applied to demonstrate the statistical properties of conventional study designs when treatments offer heterogeneous benefit as well as identify optimal designs devised to monitor the potential for heterogeneity among patients with differing clinical indications using Bayesian modeling.
Indexed as
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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.