Power and Design Issues in Crossover-Based N-Of-1 Clinical Trials with Fixed Data Collection Periods

Abstract

“N-of-1,” or single subject, clinical trials seek to determine if an intervention strategy is more efficacious for an individual than an alternative based on an objective, empirical, and controlled study. The design of such trials is typically rooted in a simple crossover strategy with multiple intervention response evaluation periods. The effect of serial correlation between measurements, the number of evaluation periods, the use of washout periods, heteroscedasticity (i.e., unequal variances among responses to the interventions) and intervention-associated carry-over phenomena on the power of such studies is crucially important for putting the yield and feasibility of N-of-1 trial designs into context. We evaluated the effect of these phenomena on the power of different designs for N-of-1 trials using analytical theory based on standard likelihood principles assuming an autoregressive lag 1, i.e., AR(1), serial correlation structure among the measurements as well as simulation studies. By evaluating the power to detect effects in many different settings, we show that the influence of serial correlation and heteroscedasticity on power can be substantial, but can also be mitigated to some degree through the use of appropriate multiple evaluation periods. We also show that the detection of certain types of carry-over effects can be heavily influenced by design considerations as well.

Pilot study in patients with congenital low-flow vascular malformation treated with low dose sirolimus

“…As vascular malformations are rare and heterogenous, the use of a CDR design offers the opportunity to generate interpretable data on e cacy and safety by analyzing each patient as their own control. We consider this design to be suited to investigate the e cacy of new treatments in those rare diseases, for which randomized clinical trials are less feasible due to the low numbers of patients affected and double blind randomized repeated rechallenges (as implemented in 'n = 1' trials) are not possible due to carry-over effects (34).…”

N-of-1 Trials in Healthcare

“…Several articles in this Special Issue discuss important statistical issues. Wang and Schork [ 9 ] used analytical and simulation studies to look at the effect of serial correlation, number of periods/phases, the presence of washout periods, and heteroscedasticity on statistical power in non-randomised AB (or further permutations) alternation designs (where ‘A’ is baseline and ‘B’ is treatment). They showed that the power to detect an effect of the intervention decreased as the strength of serial correlation increased across all the alternation designs they tested.…”

Some t-tests for N-of-1 trials with serial correlation

“…Other than Rochon (1990), all of the authors reviewed in Section 1 considered power for their proposed methods, but only for specified sample sizes and/or effect sizes (McKnight et al, 2000;Borckardt et al, 2008;Lin et al, 2016). Wang & Schork (2019) followed up on Rochon's work, investigating power, but only considered trials with 𝑚 = 400. None of the aforementioned authors gave guidance on how to compute sample sizes.…”