ArticleOrphanet journal of rare diseases2021
Hierarchical Bayesian modelling of disease progression to inform clinical trial design in centronuclear myopathy.
Article in Orphanet journal of rare diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 30 citations in OpenAlex.
- From Small Data to Big Decisions: How Clinical Pharmacology Shapes Rare Disease Development.Journal of clinical pharmacology · 2026Review
- Empowering Clinical Development With Disease Progression Modeling: Recommendations From the Clinical Trials Transformation Initiative.Clinical and translational science · 2026Article
- State-of-the-Art on Model-Informed Drug Development Approaches for Pediatric Rare Diseases.CPT: pharmacometrics & systems pharmacology · 2025Review
- The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making.Clinical pharmacology and therapeutics · 2025Review
- Biomarkers of disease progression in progressive supranuclear palsy for use in clinical trials.Brain communications · 2025Review
- Review
- Development of a Predictive Statistical Pharmacological Model for Local Anesthetic Agent Effects with Bayesian Hierarchical Model Parameter Estimation.Medicines (Basel, Switzerland) · 2023Article
- Development of tailored splice-switching oligonucleotides for progressive brain disorders in Europe: development, regulation, and implementation considerations.RNA (New York, N.Y.) · 2023Article
- Role of Disease Progression Models in Drug Development.Pharmaceutical research · 2022Review
- Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles.Orphanet journal of rare diseases · 2022Review
- Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.Clinical and translational science · 2022Review
- Landscape analysis for a neonatal disease progression model of bronchopulmonary dysplasia: Leveraging clinical trial experience and real-world data.Frontiers in pharmacology · 2022Article
- The LORIS MyeliNeuroGene rare disease database for natural history studies and clinical trial readiness.Orphanet journal of rare diseases · 2021Article
- Lessons Learned from Discontinued Clinical Developments in Duchenne Muscular Dystrophy.Frontiers in pharmacology · 2021Article
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Authors and funding
14 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCentronuclear myopathies are severe rare congenital diseases. The clinical variability and genetic heterogeneity of these myopathies result in major challenges in clinical trial design. Alternative strategies to large placebo-controlled trials that have been used in other rare diseases (e.g., the use of surrogate markers or of historical controls) have limitations that Bayesian statistics may address. Here we present a Bayesian model that uses each patient's own natural history study data to predict progression in the absence of treatment. This prospective multicentre natural history evaluated 4-year follow-up data from 59 patients carrying mutations in the MTM1 or DNM2 genes.
methodsOur approach focused on evaluation of forced expiratory volume in 1 s (FEV1) in 6- to 18-year-old children. A patient was defined as a responder if an improvement was observed after treatment and the predictive probability of such improvement in absence of intervention was less than 0.01. An FEV1 response was considered clinically relevant if it corresponded to an increase of more than 8%.
resultsThe key endpoint of a clinical trial using this model is the rate of response. The power of the study is based on the posterior probability that the rate of response observed is greater than the rate of response that would be observed in the absence of treatment predicted based on the individual patient's previous natural history. In order to appropriately control for Type 1 error, the threshold probability by which the difference in response rates exceeds zero was adapted to 91%, ensuring a 5% overall Type 1 error rate for the trial.
conclusionsBayesian statistical analysis of natural history data allowed us to reliably simulate the evolution of symptoms for individual patients over time and to probabilistically compare these simulated trajectories to actual observed post-treatment outcomes. The proposed model adequately predicted the natural evolution of patients over the duration of the study and will facilitate a sufficiently powerful trial design that can cope with the disease's rarity. Further research and ongoing dialog with regulatory authorities are needed to allow for more applications of Bayesian statistics in orphan disease research.
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