ReviewTherapeutic innovation & regulatory science2023
Bayesian Strategies in Rare Diseases.
Review in Therapeutic innovation & regulatory science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- A narrative review of the economic burden of myelin oligodendrocyte glycoprotein antibody-associated disease and analogous conditions.Frontiers in neurology · 2025Pooled it
- From Small Data to Big Decisions: How Clinical Pharmacology Shapes Rare Disease Development.Journal of clinical pharmacology · 2026Review
- Pruritus is common in primary sclerosing cholangitis, persists over time, and its intensity is associated with disease severity: a multicentre, prospective observational study.Hepatology (Baltimore, Md.) · 2026Observational
- Navigating challenges in pediatric trial conduct: integrating bayesian sequential design with semiparametric elicitation for handling primary and secondary endpoints.BMC medical research methodology · 2025Article
- Modeling frameworks in nutritional epidemiology matter: comparing isotemporal and time-lagged Bayesian and frequentist approaches of carbohydrate intake and adiposity.Frontiers in nutrition · 2025Article
- Empagliflozin in paediatric heart failure: model-based optimisation of a pharmacokinetic bridging study.Frontiers in medicine · 2025Article
- Use of Bayesian approaches in oncology clinical trials: A cross-sectional analysis.Frontiers in pharmacology · 2025Article
- Logistic regression and other statistical tools in diagnostic biomarker studies.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Review
- A Tutorial on Modern Bayesian Methods in Clinical Trials.Therapeutic innovation & regulatory science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bayesian strategies for planning and analyzing clinical trials have become a viable choice, especially in rare diseases where drug development faces many challenges and stakeholders are interested in innovations that may help overcome them. Disease natural history and clinical outcomes occurrence and variability are often poorly understood. Standard trial designs are not optimized to obtain adequate safety and efficacy data from small numbers of patients. Bayesian methods are well-suited for adaptive trials, with an accelerated learning curve. Using Bayesian statistics can be advantageous in that design choices and their consequences are considered carefully, continuously monitored, and updated where necessary, which ultimately provides a natural and principled way of seamlessly combining prior clinical information with data, within a solid decision theoretical framework. In this article, we introduce the Bayesian option in the rare disease context to support clinical decision-makers in selecting the best choice for their drug development project. Many researchers in drug development show reluctance to using Bayesian statistics, and the top-two reported barriers are insufficient knowledge of Bayesian approaches and a lack of clarity or guidance from regulators. Here we introduce concepts of borrowing, extrapolation, adaptation, and modeling and illustrate them with examples that have been discussed or developed with regulatory bodies to show how Bayesian strategies can be applied to drug development in rare diseases.
Indexed as
Identifiers
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.