Hierarchical Bayesian modelling of disease progression to inform clinical trial design in centronuclear myopathy
Fouarge E., Monseur A., Boulanger B., Annoussamy M., Seferian AM., De Lucia S., Lilien C., Thielemans L., Paradis K., Cowling BS., Freitag C., Carlin BP., Servais L., Gidaro T., Gargaun E., Chê V., Schara U., Gangfuß A., D’Amico A., Dowling JJ., Darras BT., Daron A., Hernandez A., de Lattre C., Arnal JM., Mayer M., Cuisset JM., Vuillerot C., Fontaine S., Bellance R., Biancalana V., Buj-Bello A., Hogrel JY., Landy H., Amburgey K., Andres B., Bertini E., Cardas R., Denis S., Duchêne D., Latournerie V., Reguiba N., Tsuchiya E., Wallgren-Pettersson C.
© 2021, The Author(s). 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.