Designing Quasi-Experimental Single Participant Studies for Targeted Genetic Therapies with a One-Off Impact

Background: Genetic therapies aim to create targeted interventions that treat, prevent or manage genetic disorders, holding substantial promise for many pediatric diseases with underlying genetic causes. As emerging genetic therapies are increasingly being tailored to individual patients, evaluating their efficacy is challenging. Standard N-of-1 trial designs that require repeated crossovers between experimental and control conditions are infeasible as many genetic therapies produce one-time, irreversible effects. Quasi-experimental single-participant designs with pre- and post- intervention periods are a promising alternative in this context where interrupted time series (ITS) analysis can be applied. Herein, we evaluated and compared different design and statistical elements for single participant studies of novel genetic therapies. Methods: We simulated designs using pre- and post-intervention treatment periods examined with ITS models under different assumptions about treatment effects. Changes in a continuous study outcome were assumed to be linear with varying standard deviations (SD). Different frequentist and Bayesian statistical models were compared. Trial performance was evaluated across varying pre-treatment lengths, effect sizes, outcome measurement frequencies, and outcome SD. Patient and family engagement examined the feasibility of study design elements. Results: Using frequentist methods, three selected statistical models achieved similar optimal performance for a study duration of 2 years, yielding power of approximately 80% or higher while maintaining type I error rates around 5%. Simulations suggested studies with balanced pre-post intervention period lengths, larger treatment effects, lower outcome SD, and more frequent outcome assessments had the highest statistical power. Type I error rates were insensitive to outcome SD changes. Less frequent outcome measurement improved trial feasibility from a family perspective. Similar performance was observed when applying Bayesian analyses with minimally informative priors under the same trial design assumptions. Incorporating patient and caregiver perspectives identified additional strategies to improve feasibility and acceptability of the trial designs beyond statistical considerations. Conclusions: Quasi-experimental single participant studies with ITS analysis provide a feasible framework for evaluating novel genetic treatment effect in rare diseases. Appropriate statistical approaches and design parameters are critical for achieving adequate power while maintaining type I error control. Meaningful patient and family engagement during study design is critical for feasibility.