Optimizing Aggregated N-Of-1 Trial Designs for Predictive Biomarker Validation: Statistical Methods and Theoretical Findings
| Title: | Optimizing Aggregated N-Of-1 Trial Designs for Predictive Biomarker Validation: Statistical Methods and Theoretical Findings |
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| Authors: | Rebecca C. Hendrickson, Ronald G. Thomas, Nicholas J. Schork, Murray A. Raskind |
| Source: | Frontiers in Digital Health, Vol 2 (2020) |
| Publisher Information: | Frontiers Media S.A., 2020. |
| Publication Year: | 2020 |
| Collection: | LCC:Medicine LCC:Public aspects of medicine LCC:Electronic computers. Computer science |
| Subject Terms: | N-of-1 trials, crossover trials, posttraumatic stress disorder (PTSD), prazosin, biomarkers, personalized medicine, Medicine, Public aspects of medicine, RA1-1270, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: | Background and Significance: Parallel-group randomized controlled trials (PG-RCTs) are the gold standard for detecting differences in mean improvement across treatment conditions. However, PG-RCTs provide limited information about individuals, making them poorly optimized for quantifying the relationship of a biomarker measured at baseline with treatment response. In N-of-1 trials, an individual subject moves between treatment conditions to determine their specific response to each treatment. Aggregated N-of-1 trials analyze a cohort of such participants, and can be designed to optimize both statistical power and clinical or logistical constraints, such as allowing all participants to begin with an open-label stabilization phase to facilitate the enrollment of more acutely symptomatic participants. Here, we describe a set of statistical simulation studies comparing the power of four different trial designs to detect a relationship between a predictive biomarker measured at baseline and subjects' specific response to the PTSD pharmacotherapeutic agent prazosin.Methods: Data was simulated from 4 trial designs: (1) open-label; (2) open-label + blinded discontinuation; (3) traditional crossover; and (4) open label + blinded discontinuation + brief crossover (the N-of-1 design). Designs were matched in length and assessments. The primary outcome, analyzed with a linear mixed effects model, was whether a statistically significant association between biomarker value and response to prazosin was detected with 5% Type I error. Simulations were repeated 1,000 times to determine power and bias, with varied parameters.Results: Trial designs 2 & 4 had substantially higher power with fewer subjects than open label design. Trial design 4 also had higher power than trial design 2. Trial design 4 had slightly lower power than the traditional crossover design, although power declined much more rapidly as carryover was introduced.Conclusions: These results suggest that an aggregated N-of-1 trial design beginning with an open label titration phase may provide superior power over open label or open label and blinded discontinuation designs, and similar power to a traditional crossover design, in detecting an association between a predictive biomarker and the clinical response to the PTSD pharmacotherapeutic prazosin. This is achieved while allowing all participants to spend the first 8 weeks of the trial on open-label active treatment. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2673-253X |
| Relation: | https://www.frontiersin.org/article/10.3389/fdgth.2020.00013/full; https://doaj.org/toc/2673-253X |
| DOI: | 10.3389/fdgth.2020.00013 |
| Access URL: | https://doaj.org/article/f0649e953119430d99b9b5c5c539c1c7 |
| Accession Number: | edsdoj.f0649e953119430d99b9b5c5c539c1c7 |
| Database: | Directory of Open Access Journals |
| ISSN: | 2673253X |
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| DOI: | 10.3389/fdgth.2020.00013 |
| Published in: | Frontiers in Digital Health |
| Language: | English |