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

Article


Blackston, J. Walker, Chapple, Andrew G., McGree, James M., McDonald, Suzanne and Nikles, Jane. 2019. "Comparison of Aggregated N-of-1 Trials with Parallel and Crossover Randomized Controlled Trials Using Simulation Studies." Health Care: the journal of delivery science and innovation. 7 (4), pp. 1-13. https://doi.org/10.3390/healthcare7040137
Article Title

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

ERA Journal ID212679
Article CategoryArticle
AuthorsBlackston, J. Walker (Author), Chapple, Andrew G. (Author), McGree, James M. (Author), McDonald, Suzanne (Author) and Nikles, Jane (Author)
Journal TitleHealth Care: the journal of delivery science and innovation
Journal Citation7 (4), pp. 1-13
Article Number137
Number of Pages13
Year2019
PublisherElsevier BV
Place of PublicationSwitzerland
ISSN2213-0764
2213-0772
Digital Object Identifier (DOI)https://doi.org/10.3390/healthcare7040137
Web Address (URL)https://www.mdpi.com/2227-9032/7/4/137
Abstract

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 3 cycles, which compare aggregated N-of-1 trials to parallel randomized controlled trials (RCTs) and crossover trials.

Results: Aggregated N-of-1 trials outperformed both traditional parallel RCT and crossover designs when these 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 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.

KeywordsN-of-1 trial; evidence-based medicine; comparative effectiveness; clinical trial; single-case study; simulation study; statistical methods
ANZSRC Field of Research 2020490501. Applied statistics
Byline AffiliationsTulane University, United States
Louisiana State University, United States
Queensland University of Technology
University of Queensland
Institution of OriginUniversity of Southern Queensland
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