Accommodating Serial Correlation and Sequential Design Elements in Personalized Studies and Aggregated Personalized Studies

Abstract

Single subject, or 'N-of-1,' studies are receiving a great deal of attention from both theoretical and applied researchers. This is consistent with the growing acceptance of 'personalized' approaches to health care and the need to prove that personalized interventions tailored to an individual's likely unique physiological profile and other characteristics work as they should. In fact, the preferred way of referring to N-of-1 studies in contemporary settings is as 'personalized studies.' Designing efficient personalized studies and analyzing data from them in ways that ensure statistically valid inferences are not trivial, however. I briefly discuss some of the more complex issues surrounding the design and analysis of personalized studies, such as the use of washout periods, the frequency with which measures associated with the efficacy of an intervention are collected during a study, and the serious effect that serial correlation can have on the analysis and interpretation of personalized study data and results if not accounted for explicitly. I point out that more efficient sequential designs for personalized and aggregated personalized studies can be developed, and I explore the properties of sequential personalized studies in a few settings via simulation studies. Finally, I comment on contexts within which personalized studies will likely be pursued in the future.

Exploring human biology with N-of-1 clinical trials

“…However, serial correlations between the measurements can complicate the analysis if not appropriately accounted for, as can aforementioned covariate effects, carryover effects, missing data, non-uniform time points between measurement collections, and placebo effects (Rochon 1990;Huitema 2011;Lillie et al 2011;Wang and Schork 2019;Somer, Gische, and Miocevic 2022). Many offshoots of N-of-1 trials exist to improve their efficiency and comprehensiveness; for example, sequential designs can be used to minimize the number of measurements made while preserving appropriate false positive and false negative rates (Schork 2022;Schork and Goetz 2017). In addition, there is no reason that https://doi.org/10.1017/pcm.2022.15 Published online by Cambridge University Press N-of-1 trial methodology cannot be used in other settings; for example, assessing intervention effects in cell lines, tissue samples, mice etc.…”

“…Thus, by definition, a geroprotector should affect multiple systems and hence could be tested for this. In fact, if only one or some subset of health measures among many different measures is in fact affected by a purported geroprotector, then the intervention is probably not a geroprotector (Schork et al 2022).…”

“…In addition, there is also interest in identifying 'surrogate endpoints' for clinical trials that initially focus on expensive, lengthy and logistically challenging health outcome measures, and N-of-1 trials are excellent vehicles for validating biomarkers and surrogate endpoints (Burzykowski, Molenberghs, and Buyse 2005). As an example, consider the development and use of epigenetic clocks as surrogate endpoints in trials of geroprotectors (Schork et al 2022). The belief is that if an intervention modulates or changes an epigenetic clock https://doi.org/10.1017/pcm.2022.15 Published online by Cambridge University Press among participants in a trial in positive waysthereby indicating that the intervention in question is slowing the aging rate of the individualsthen those individuals do not necessarily have to be tracked longitudinally until they develop (or do not develop) agerelated diseases that the candidate geroprotector is hypothesized to prevent or treat (Schork et al 2022;Kritchevsky and Justice 2020;Mahmoudi, Xu, and Brunet 2019).…”

“…(5) The results of clinical trials may identify interventions with the potential to benefit individuals, but unless it is known a priori how to identify individuals most likely to benefit from each intervention, it will be unclear how to optimally provide the interventions (see Figure 1 ). (6) Standard population-based RCTs can take a very long time to pursue and analyze, whereas more focused participant or patient-oriented alternative trial designs can be aggregated sequentially to enable population-based inferences (Schork, 2022 ).

Figure 1.

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The Family of Single-Case Experimental Designs

“…In the current article, we provide an overview of SCEDs and thus a context for the articles in this special issue focused on personalized (N-of-1) trials. Our focus is to provide the fundamentals of these designs, and more detailed treatments of data analysis (Moeyaert & Fingerhut, 2022;Schork, 2022) conduct and reporting standards (Kravitz & Duan, 2022;Porcino & Vohra, 2022), and other methodological considerations are provided in this special issue. Our hope is that this article will inspire a diverse array of students, engineers, scientists, and practitioners to further explore the utility, rigor, and flexibility of these designs.…”