Hierarchical Bayesian modelling of disease progression to inform clinical trial design in centronuclear myopathy

Abstract

Background Centronuclear 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. Methods Our 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%. Results The 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. Conclusions Bayesian 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.

Lessons Learned from Discontinued Clinical Developments in Duchenne Muscular Dystrophy

“…Additionally, the regulatory pressure for the use of placebo arms contradicts the wishes of the community ( Peay et al, 2018 ). The stratification of patients by disease trajectory ( Muntoni et al, 2019b ) to better define inclusion criteria, the use of natural history data to enrich placebo arms ( Bello et al, 2016 ; Goemans et al, 2020a ; Mercuri et al, 2020 ; Osorio et al, 2020 ), the sharing of placebo data via platforms ( Bello et al, 2016 ; Goemans et al, 2020a ), the use of innovative and sensitive outcome measures ( Seferian et al, 2015 ; Lilien et al, 2019 ), or the use of Bayesian modelling could all result in more efficient and informative clinical trials ( Fouarge et al, 2021 ).…”

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“…Demonstration projects for each were presented, only one of which was published in the peer-reviewed literature. 41,42 There is an opportunity to identify "digital biomarkers" based on algorithms processing various data sets, perhaps derived from wearable technology, such as smart watches, smart phones, or pedometers. 43 For example, output of wearables, such as pedometers that count steps, could be useful to monitor vulnerable elderly patients at risk for rapid deterioration.…”

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“…The goal of a natural history study is to recruit patients for longitudinal analysis of natural disease progression [12]. The data gathered is used to help identify surrogate markers, determine the best outcome measures to be used in potential therapeutic trials, can serve as the control arm and serve as benchmarks for efficacy in one arm rare disease trials [13][14][15][16][17]. Natural history studies result in incredible amounts of information being collected, including clinical, behavioral, sociodemographic, genetic, imaging, and patient and family reported outcomes.…”