ArticleBiometrics2023
SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials.
Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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
15 citing papers in PubMed.
- Self-Adapting Priors for Dynamic Borrowing in Three-Arm Non-Inferiority Trials With Pre-Specified Margin.Statistics in medicine · 2026Article
- A Bayesian Optimal Adaptive Clinical Trial Design for Sequentially Integrated Therapies.Statistics in medicine · 2026Article
- Comparative evaluation of Bayesian external information borrowing and frequentist approaches in underpowered confirmatory trials.BMC medical research methodology · 2026Article
- An E-value-Informed Sensitivity Analysis Framework for Hybrid Controlled Trials.medRxiv : the preprint server for health sciences · 2026Article
- Dual-Criterion Approach Incorporating Historical Information to Seek Accelerated Approval With Application in Time-to-Event Group Sequential Trials.Statistics in medicine · 2026Article
- Bayesian Prediction of Event Times Using Mixture Model for Blinded Randomized Controlled Trials.Statistics in medicine · 2025Article
- Commensurate prior models with random effects for interval-censored data to accommodate historical controls.Communications in statistics: Simulation and computation · 2025Article
- Insights on Clinical Development of Cell and Gene Therapy for Rare Diseases-by DahShu Innovative Design Scientific Working Group (IDSWG).Therapeutic innovation & regulatory science · 2025Review
- A bayesian dynamic borrowing framework for improving the efficiency of clinical trials.BMC medical research methodology · 2025Article
- PS-SAM: propensity-score-integrated self-adapting mixture prior to dynamically and efficiently borrow information from historical data.Journal of biopharmaceutical statistics · 2025Article
- Bayesian dynamic borrowing in group-sequential design for medical device studies.BMC medical research methodology · 2025Article
- A generalized phase 1-2-3 design integrating dose optimization with confirmatory treatment comparison.Biometrics · 2024Article
- Considerations on Design and Analysis of External Control in Pediatric Oncology.Medical research archives · 2024Article
- Treatment Comparisons in Adaptive Platform Trials Adjusting for Temporal Drift.Statistics in biopharmaceutical research · 2024Article
- Bayesian approach for design and analysis of medical device trials in the era of modern clinical studies.Medical review (2021) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Mixture priors provide an intuitive way to incorporate historical data while accounting for potential prior-data conflict by combining an informative prior with a noninformative prior. However, prespecifying the mixing weight for each component remains a crucial challenge. Ideally, the mixing weight should reflect the degree of prior-data conflict, which is often unknown beforehand, posing a significant obstacle to the application and acceptance of mixture priors. To address this challenge, we introduce self-adapting mixture (SAM) priors that determine the mixing weight using likelihood ratio test statistics or Bayes factors. SAM priors are data-driven and self-adapting, favoring the informative (noninformative) prior component when there is little (substantial) evidence of prior-data conflict. Consequently, SAM priors achieve dynamic information borrowing. We demonstrate that SAM priors exhibit desirable properties in both finite and large samples and achieve information-borrowing consistency. Moreover, SAM priors are easy to compute, data-driven, and calibration-free, mitigating the risk of data dredging. Numerical studies show that SAM priors outperform existing methods in adopting prior-data conflicts effectively. We developed R package "SAMprior" and web application that are freely available at CRAN and www.trialdesign.org to facilitate the use of SAM priors.
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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.