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

Journal article

Using external controls based on real-world or natural history data (RWD/NHD) for drug evaluations in Duchenne muscular dystrophy (DMD) is appealing given the challenges of enrolling placebo-controlled trials, especially for multi-year trials. Comparisons to external controls, however, face risks of bias due to differences in outcomes between trial and RWD/NHD settings. To assess this bias empirically, we conducted a multi-institution study comparing mean 48-week changes in North Star Ambulatory Assessment (NSAA) total score between trial placebo arms and RWD/NHD sources, with and without adjustment for baseline prognostic factors. Analyses used data from three placebo arms (235 48-week intervals, N = 235 patients) and three RWD/NHD sources (348 intervals, N = 202 patients). Differences in mean ΔNSAA between placebo arms and RWD/NHD sources were small before adjustment (-1.2 units, 95% CI: [-2.0 -0.5]) and were attenuated and no longer statistically significant after adjustment (0.1 units (95% CI: [-0.6, 0.8]). Results were similar whether adjusting using multivariable regression or propensity score matching. This consistency in ΔNSAA between trial placebo arms and RWD/NHD sources accords with prior findings for the six-minute walk distance, provides a well-validated framework for baseline adjustment of prognostic factors, and supports the suitability of RWD/NHD external controls for drug evaluations in ambulatory DMD

Authors: Fouarge, E; Monseur, A; Boulanger, B; Annoussamy, M; Seferian, AM

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: Orphanet Journal of Rare Diseases
• Volume: 16
• Issue: 1
• Pages: 3-3
• Publication date: 2021-01-06
• DOI: 10.1186/s13023-020-01663-7
• EISSN: 1750-1172
• ISSN: 1750-1172
• Language: English
• Keywords: Medicine; Computer science; Myopathy; Internal medicine; Artificial intelligence; Biology; Bioinformatics; Bayesian probability; Clinical trial; Disease