ArticleBiostatistics (Oxford, England)2019
Bayesian clinical trial design using historical data that inform the treatment effect.
Article in Biostatistics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed, 86 citations in OpenAlex.
- Borrowing using historical-bias power prior with empirical Bayes.Journal of biopharmaceutical statistics · 2026Article
- Multivariate Bayesian Dynamic Borrowing for Repeated Measures Data With Application to External Control Arms in Open-Label Extension Studies.Biometrical journal. Biometrische Zeitschrift · 2025Article
- New clinical trial design borrowing information across patient subgroups based on fusion-penalized regression models.Statistical methods in medical research · 2024Article
- A Bayesian platform trial design with hybrid control based on multisource exchangeability modelling.Statistics in medicine · 2024Article
- Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review.BMC medical research methodology · 2024Review
- Article
- Bayesian Methods: A Means of Improving Statistical Power in Preclinical Neurotrauma?Neurotrauma reports · 2024Article
- Development and Evaluation of a Simulation-Based Algorithm to Optimize the Planning of Interim Analyses for Clinical Trials in ALS.Neurology · 2023Article
- Bayesian Statistics for Medical Devices: Progress Since 2010.Therapeutic innovation & regulatory science · 2023Review
- The scale transformed power prior for use with historical data from a different outcome model.Statistics in medicine · 2023Article
- When we shouldn't borrow information from an existing network of trials for planning a new trial.Frontiers in pharmacology · 2023Article
- Bayesian multivariate probability of success using historical data with type I error rate control.Biostatistics (Oxford, England) · 2022Article
- Innovative trial design in precision oncology.Seminars in cancer biology · 2022Review
- Bayesian design of clinical trials using joint models for longitudinal and time-to-event data.Biostatistics (Oxford, England) · 2022Article
- A decision-theoretic approach to Bayesian clinical trial design and evaluation of robustness to prior-data conflict.Biostatistics (Oxford, England) · 2022Article
- New approaches for testing non-inferiority for three-arm trials with Poisson distributed outcomes.Biostatistics (Oxford, England) · 2022Article
- Revisiting a Null Hypothesis: Exploring the Parameters of Oligometastasis Treatment.International journal of radiation oncology, biology, physics · 2021Article
- Assessing efficacy in important subgroups in confirmatory trials: An example using Bayesian dynamic borrowing.Pharmaceutical statistics · 2021Article
- A novel equivalence probability weighted power prior for using historical control data in an adaptive clinical trial design: A comparison to standard methods.Pharmaceutical statistics · 2021Article
- Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach.International journal of environmental research and public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
We consider the problem of Bayesian sample size determination for a clinical trial in the presence of historical data that inform the treatment effect. Our broadly applicable, simulation-based methodology provides a framework for calibrating the informativeness of a prior while simultaneously identifying the minimum sample size required for a new trial such that the overall design has appropriate power to detect a non-null treatment effect and reasonable type I error control. We develop a comprehensive strategy for eliciting null and alternative sampling prior distributions which are used to define Bayesian generalizations of the traditional notions of type I error control and power. Bayesian type I error control requires that a weighted-average type I error rate not exceed a prespecified threshold. We develop a procedure for generating an appropriately sized Bayesian hypothesis test using a simple partial-borrowing power prior which summarizes the fraction of information borrowed from the historical trial. We present results from simulation studies that demonstrate that a hypothesis test procedure based on this simple power prior is as efficient as those based on more complicated meta-analytic priors, such as normalized power priors or robust mixture priors, when all are held to precise type I error control requirements. We demonstrate our methodology using a real data set to design a follow-up clinical trial with time-to-event endpoint for an investigational treatment in high-risk melanoma.
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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.