Article describes how authors examined publications of medical N-of-1 trials to examine whether they meet the evidence standards and the criteria for demonstrating evidence of a relation between an independent and an outcome variable per the What Works Clearinghouse (WWC) standards for SCEDs. The authors also examined the appropriateness of the data analytic techniques in the special context of N-of-1 designs.
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Article describes how authors examined publications of medical N-of-1 trials to examine whether they meet the evidence standards and the criteria for demonstrating evidence of a relation between an independent and an outcome variable per the What Works Clearinghouse (WWC) standards for SCEDs. The authors also examined the appropriateness of the data analytic techniques in the special context of N-of-1 designs.
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Abstract: N-of-1 trials, a special case of Single Case Experimental Designs (SCEDs), are prominent in clinical medical research and specifically psychiatry due to the growing significance of precision/personalized medicine. It is imperative that these clinical trials be conducted, and their data analyzed, using the highest standards to guard against threats to validity. This systematic review examined publications of medical N-of-1 trials to examine whether they meet (a) the evidence standards and (b) the criteria for demonstrating evidence of a relation between an independent and an outcome variable per the What Works Clearinghouse (WWC) standards for SCEDs. We also examined the appropriateness of the data analytic techniques in the special context of N-of-1 designs. We searched for empirical journal articles that used N-of-1 design and published between 2013 and 2022 in PubMed and Web of Science. Protocols or methodological papers and studies that did not manipulate a medical condition were excluded. We reviewed 115 articles; 4 (3.48%) articles met all WWC evidence standards. Most (99.1%) failed to report an appropriate design-comparable effect size; neither did they report a confidence/credible interval, and 47.9% reported neither the raw data rendering meta-analysis impossible. Most (83.8%) ignored autocorrelation and did not meet distributional assumptions (65.8%). These methodological problems could lead to significantly inaccurate effect sizes. It is necessary to implement stricter guidelines for the clinical conduct and analyses of medical N-of-1 trials. Reporting neither raw data nor design-comparable effect sizes renders meta-analysis impossible and is antithetical to the spirit of open science.
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Batley, Prathiba Natesan; McClure, Erica B.; Brewer, Brandy; Contractor, Ateka A.; Batley, Nicholas John; Hedges, Larry Vernon et al.Evidence and reporting standards in N-of-1 medical studies: a systematic review,
article,
July 18, 2023;
(https://digital.library.unt.edu/ark:/67531/metadc2288932/:
accessed June 28, 2024),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT College of Liberal Arts & Social Sciences.
