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Open AccessArticle

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

1
Model Risk Management, USAA, 9257 Delaney Creek Blv, Tampa, FL 33619, USA
2
Quantitative Medicine and Systems Biology, The Translational Genomics Research Institute (TGen), an affiliate of the City of Hope National Medical Center, 445 North Fifth Street, Phoenix, AZ 85004, USA
3
The Joint City of Hope/TGen IMPACT Center (NJS), Duarte, CA 91010, USA
*
Author to whom correspondence should be addressed.
Healthcare 2019, 7(3), 84; https://doi.org/10.3390/healthcare7030084
Received: 6 May 2019 / Revised: 21 June 2019 / Accepted: 30 June 2019 / Published: 2 July 2019
(This article belongs to the Special Issue N-of-1 Trials in Healthcare)
“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. View Full-Text
Keywords: Efficacy; linear models; interventions; human clinical studies Efficacy; linear models; interventions; human clinical studies
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Wang, Y.; Schork, N.J. Power and Design Issues in Crossover-Based N-Of-1 Clinical Trials with Fixed Data Collection Periods. Healthcare 2019, 7, 84.

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