Evaluating longitudinal treatment effects for Duchenne muscular dystrophy using dynamically enriched Bayesian small sample, sequential, multiple assignment randomized trial (snSMART).

Abstract

For progressive rare diseases like Duchenne muscular dystrophy (DMD), evaluating disease burden by measuring the totality of evidence from outcome data over time per patient can be highly informative, especially regarding how a new treatment impacts disease progression and functional outcomes. This paper focuses on new statistical approaches for analyzing data generated over time in a small sample, sequential, multiple assignment, randomized trial (snSMART), with an application to DMD. In addition, the use of external control data can enhance the statistical and operational efficiency in rare disease drug development by solving participant scarcity issues and ethical challenges. We employ a two-step robust meta-analytic approach to leverage external control data while adjusting for important baseline confounders and potential conflicts between external controls and trial data. Furthermore, our approach integrates important baseline covariates to account for patient heterogeneity and introduces a novel piecewise model to manage stage-wise treatment assignments. By applying this methodology to a case study in DMD research, we not only demonstrate the practical application and benefits of our approach but also highlight its potential to mitigate challenges in rare disease trials. Our findings advocate for a more nuanced and statistically robust analysis of treatment effects, thereby improving the reliability of clinical trial results.