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N-of-1 Trials as a Decision Support Tool in Clinical Practice: A Protocol for a Systematic Literature Review and Narrative Synthesis
Open AccessArticle

Comparison of Aggregated N-of-1 Trials with Parallel and Crossover Randomized Controlled Trials Using Simulation Studies

1
Department of Epidemiology, Tulane University School of Public Health & Tropical Medicine, New Orleans, LA 70112, USA
2
Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA
3
School of Mathematical Sciences, Queensland University of Technology, Brisbane 2434, Australia
4
UQCCR, The University of Queensland, Brisbane 4006, Australia
*
Author to whom correspondence should be addressed.
Healthcare 2019, 7(4), 137; https://doi.org/10.3390/healthcare7040137
Received: 14 October 2019 / Revised: 23 October 2019 / Accepted: 4 November 2019 / Published: 6 November 2019
(This article belongs to the Special Issue N-of-1 Trials in Healthcare)
Background: N-of-1 trials offer an innovative approach to delivering personalized clinical care together with population-level research. While increasingly used, these methods have raised some statistical concerns in the healthcare community. Methods: We discuss concerns of selection bias, carryover effects from treatment, and trial data analysis conceptually, then rigorously evaluate concerns of effect sizes, power, and sample size through simulation study. Four variance structures for patient heterogeneity and model error are considered in a series of 5000 simulated trials with three cycles, which compare N-of-1 trials to parallel randomized controlled trials (RCTs) and crossover trials. Results: N-of-1 trials outperformed both traditional parallel RCTs and crossover designs when trial designs were simulated in terms of power and required sample size to obtain a given power. N-of-1 designs resulted in a higher type-I error probability than parallel RCT and cross over designs when moderate-to-strong carryover or washout effects were not considered or in the presence of modeled selection bias. However, N-of-1 designs allowed better estimation of patient-level random effects. These results reinforce the need to account for these factors when planning N-of-1 trials. Conclusion: N-of-1 trial designs offer a rigorous method for advancing personalized medicine and healthcare with the potential to minimize costs and resources. Interventions can be tested with adequate power with far fewer patients than traditional RCT and crossover designs. Operating characteristics compare favorably to both traditional RCT and crossover designs. View Full-Text
Keywords: N-of-1 trial; evidence-based medicine; comparative effectiveness; clinical trial; single-case study; simulation study; statistical methods N-of-1 trial; evidence-based medicine; comparative effectiveness; clinical trial; single-case study; simulation study; statistical methods
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Blackston, J.W.; Chapple, A.G.; McGree, J.M.; McDonald, S.; Nikles, J. Comparison of Aggregated N-of-1 Trials with Parallel and Crossover Randomized Controlled Trials Using Simulation Studies. Healthcare 2019, 7, 137.

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