ArticleClinical trials (London, England)2014
The utility of Bayesian predictive probabilities for interim monitoring of clinical trials.
Article in Clinical trials (London, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 3 of them syntheses that pooled it.
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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
53 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Pooled it
- Developing a Bayesian hierarchical model for a prospective individual patient data meta-analysis with continuous monitoring.BMC medical research methodology · 2023Pooled it
- Prospective individual patient data meta-analysis: Evaluating convalescent plasma for COVID-19.Statistics in medicine · 2021Pooled it
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- A flexible multi-metric Bayesian framework for decision-making in Phase II multi-arm multi-stage studies.Statistics in medicine · 2024Trial
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- Improving Lung Function in Severe Heterogenous Emphysema with the Spiration Valve System (EMPROVE). A Multicenter, Open-Label Randomized Controlled Clinical Trial.American journal of respiratory and critical care medicine · 2019Trial
- N2M2 (NOA-20) phase I/II trial of molecularly matched targeted therapies plus radiotherapy in patients with newly diagnosed non-MGMT hypermethylated glioblastoma.Neuro-oncology · 2019Trial
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- The Stroke Hyperglycemia Insulin Network Effort (SHINE) trial: an adaptive trial design case study.Trials · 2015Trial
- Perspectives on the Use of a Bayesian Approach in Designing Seamless Phase 2/3 Clinical Trials with Dose Selection, Early Stopping for Futility and Efficacy, or Sample Size Re-estimation.Therapeutic innovation & regulatory science · 2026Article
- Barriers to Adopting Evidence From Bayesian Adaptive Clinical Trials in Critical Care: A Randomized Experiment With US Intensivists.CHEST critical care · 2026Article
- Pursuing Reduction in Fatigue After COVID-19 via Exercise and Rehabilitation (PREFACER): a protocol for a randomised feasibility trial.BMJ open · 2025Article
- Adaptive Design with Bayesian Informed Interim Decisions: Application To a Randomized Trial of Mechanical Circulatory Support.Therapeutic innovation & regulatory science · 2025Article
- Benchmarking non-inferiority design and analysis through the Z-score.European journal of clinical pharmacology · 2025Article
- Futility Monitoring in Clinical Trials.Statistics in medicine · 2025Article
- Rationale and design of the PE-TRACT trial: A multicenter randomized trial to evaluate catheter-directed therapy for the treatment of intermediate-risk pulmonary embolism.American heart journal · 2025Article
- Improving the Effectiveness of Sample Size Re-Estimation: An Operating Characteristic Focused, Hybrid Frequentist-Bayesian Approach.Statistics in medicine · 2025Article
- A tutorial on the what, why, and how of Bayesian analysis: Estimating mood and anxiety disorder prevalence using a Canadian data linkage study.PLOS mental health · 2025Article
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Funding
Abstract
backgroundBayesian predictive probabilities can be used for interim monitoring of clinical trials to estimate the probability of observing a statistically significant treatment effect if the trial were to continue to its predefined maximum sample size. PURPOSE: We explore settings in which Bayesian predictive probabilities are advantageous for interim monitoring compared to Bayesian posterior probabilities, p-values, conditional power, or group sequential methods.
resultsFor interim analyses that address prediction hypotheses, such as futility monitoring and efficacy monitoring with lagged outcomes, only predictive probabilities properly account for the amount of data remaining to be observed in a clinical trial and have the flexibility to incorporate additional information via auxiliary variables. LIMITATIONS: Computational burdens limit the feasibility of predictive probabilities in many clinical trial settings. The specification of prior distributions brings additional challenges for regulatory approval.
conclusionsThe use of Bayesian predictive probabilities enables the choice of logical interim stopping rules that closely align with the clinical decision-making process.
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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.