ArticleComputational statistics & data analysis2017
A Bayesian adaptive design for clinical trials in rare diseases.
Article in Computational statistics & data analysis, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges.International journal of environmental research and public health · 2021Pooled it
- A Bayesian analysis of finerenone in heart failure with mildly reduced and preserved ejection fraction: a pre-specified analysis of FINEARTS-HF.European heart journal. Cardiovascular pharmacotherapy · 2026Trial
- From Small Data to Big Decisions: How Clinical Pharmacology Shapes Rare Disease Development.Journal of clinical pharmacology · 2026Review
- TeloNet is born: why all specialities need to be aware of telomere biology disorders.Frontiers in medicine · 2026Review
- Adaptive Universal Principles for Real-world Observational Studies (AUPROS): an approach to designing real-world observational studies for clinical, epidemiologic, and precision oncology research.British journal of cancer · 2025Review
- Key challenges in developing a gene therapy for Usher syndrome: machine-assisted scoping review.Journal of community genetics · 2024Article
- Using biomarkers to allocate patients in a response-adaptive clinical trial.Communications in statistics: Simulation and computation · 2023Article
- Response-adaptive randomization in clinical trials: from myths to practical considerations.Statistical science : a review journal of the Institute of Mathematical Statistics · 2023Article
- A novel statistical test for treatment differences in clinical trials using a response-adaptive forward-looking Gittins Index Rule.Biometrics · 2023Article
- Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates.Journal of statistical computation and simulation · 2023Article
- Generalisations of a Bayesian decision-theoretic randomisation procedure and the impact of delayed responses.Computational statistics & data analysis · 2022Article
- Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles.Orphanet journal of rare diseases · 2022Review
- Article
- Quantifying Efficiency Gains of Innovative Designs of Two-Arm Vaccine Trials for COVID-19 Using an Epidemic Simulation Model.Statistics in biopharmaceutical research · 2022Article
- Addressing the implementation gap in advanced therapeutics for spinal muscular atrophy in the era of newborn screening programs.Frontiers in neurology · 2022Article
- Power Constrained Bandits.Proceedings of machine learning research · 2021Article
- Response adaptive designs for Phase II trials with binary endpoint based on context-dependent information measures.Computational statistics & data analysis · 2021Article
- The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design.BMJ (Clinical research ed.) · 2020Article
- Article
- A response-adaptive randomization procedure for multi-armed clinical trials with normally distributed outcomes.Biometrics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Development of treatments for rare diseases is challenging due to the limited number of patients available for participation. Learning about treatment effectiveness with a view to treat patients in the larger outside population, as in the traditional fixed randomised design, may not be a plausible goal. An alternative goal is to treat the patients within the trial as effectively as possible. Using the framework of finite-horizon Markov decision processes and dynamic programming (DP), a novel randomised response-adaptive design is proposed which maximises the total number of patient successes in the trial and penalises if a minimum number of patients are not recruited to each treatment arm. Several performance measures of the proposed design are evaluated and compared to alternative designs through extensive simulation studies using a recently published trial as motivation. For simplicity, a two-armed trial with binary endpoints and immediate responses is considered. Simulation results for the proposed design show that: (i) the percentage of patients allocated to the superior arm is much higher than in the traditional fixed randomised design; (ii) relative to the optimal DP design, the power is largely improved upon and (iii) it exhibits only a very small bias and mean squared error of the treatment effect estimator. Furthermore, this design is fully randomised which is an advantage from a practical point of view because it protects the trial against various sources of bias. As such, the proposed design addresses some of the key issues that have been suggested as preventing so-called bandit models from being implemented in clinical practice.
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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.