Evidence and reporting standards in N-of-1 medical studies: a systematic review

• Published in: Translational psychiatry Vol. 13; no. 1; p. 263
• Main Authors: Natesan Batley, Prathiba, McClure, Erica B., Brewer, Brandy, Contractor, Ateka A., Batley, Nicholas John, Hedges, Larry Vernon, Chin, Stephanie
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.07.2023; Nature Publishing Group
• Subjects: 631/477/2811; 706/648; Behavioral Sciences; Biological Psychology; Biomedical Research; Medicine; Medicine & Public Health; Neurosciences; Pharmacotherapy; Psychiatry; Research Design; Systematic Review
• ISSN: 2158-3188; 2158-3188
• DOI: 10.1038/s41398-023-02562-8

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.