Continuous Outcome Estimation in N-of-1 Trials for Accelerated Decision-Making

N-of-1 trials aim to determine the therapeutic effect for a single individual. This individualized approach necessitates collecting multiple data points over time through repeated alternating periods of active treatment and a comparator or control condition. The extended duration of the treatment periods may increase patient burden, prolong placebo exposure, and increase the likelihood of study discontinuation. In theory, treatment responders (or non-responders) can be identified early during the trial if the therapeutic effect is strong (or completely lacking). There are no theoretical constraints to evaluate treatment efficacy more regularly – not only after a predetermined number of treatment periods – given that the individualized character of the N-of-1 study permits a statistical model update as soon as new data becomes accessible. Regularly updating estimates on treatment effects allows clinicians to accelerate clinical decision-making regarding N-of-1 study termination. This study examines the importance of continuous treatment effect estimation in N-of-1 trials through simulation and re-analysis of existing trial datasets of neurological diseases. Results indicate that treatment efficacy decisions can be expedited when outcome estimation is performed continuously rather than delayed until the end of the trial.