Patient preferences for personalized (N-of-1) trials: a conjoint analysis

Abstract Objective Despite their promise for increasing treatment precision, Personalized Trials (i.e., N-of-1 trials) have not been widely adopted. We aimed to ascertain patient preferences for Personalized Trials. Study Design and Setting We recruited 501 adults with ≥2 common chronic conditions from Harris Poll Online. We used Sawtooth Software to generate 45 plausible Personalized Trial designs comprising combinations of eight key attributes (treatment selection, treatment type, clinician involvement, blinding, time commitment, self-monitoring frequency, duration, and cost) at different levels. Conditional logistic regression was used to assess relative importance of different attributes using a random utility maximization model. Results Overall, participants preferred Personalized Trials with no costs vs. $100 cost (utility difference 1.52 [standard error 0.07], P P P all Conclusion To optimize dissemination, Personalized Trial designers should seek to minimize out-of-pocket costs and time burden of self-monitoring. They should also consider adaptive designs that can accommodate subgroup differences in design preferences.