Objective:To review the feasibility and effectiveness ofn-of-1 randomized controlled trials (n-of-1 trials) in clinical practice. Design:Individual trials were double-blind, randomized, mu...

The n-of-1 Randomized Controlled Trial: Clinical Usefulness

Single-case experimental designs (SCEDs) have proven invaluable in research and practice because they are optimal for asking many experimental questions relevant to the analysis of behavior. The consecutive controlled case series (CCCS) is a type of study in which a SCED is employed in a series of consecutively encountered cases that undergo a common procedure or share a common characteristic. Additional design elements, data-analytic, and reporting methods enable researchers to ask experimental questions relevant to the study of generality of procedures and processes. The current paper discusses the CCCS methodologies, including the retrospective, prospective, and randomized CCCS. These methodologies can be applied to examine the generality of clinical procedures (including their general efficacy, the limits of their generality, and variables that may mediate generality); study the epidemiology and phenomenology of clinical problems; and compare the efficacy of 2 clinical procedures within a randomized controlled trial combining SCEDs with randomized group designs.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/jaba.691

The consecutive controlled case series: Design, data-analytics, and reporting methods supporting the study of generality.

The technology currently available for quantifying various biometric, behavioral, emotional, cognitive and psychological aspects of daily life has become increasingly diverse, accurate and accessible. Continued improvements are ongoing. These burgeoning technologies can and will profoundly alter the way lifestyle, health, wellness and chronic diseases are managed in the future. For those pursuing the potential of such digital technologies in the creation of compelling and effective connected healthcare experiences, a number of new concepts have surfaced. We have taken these concepts (many of which originate in engineering) and extended them to be incorporated into managing health risk and health conditions via a blended digital health experience. For example, the advent of mobile technology for health has given rise to concepts such as ecological momentary assessment (EMA) and ecological momentary intervention (EMI) that assess the person’s (digital twin) status and delivers interventions as needed when needed. For such concepts to be fully realized the experience design of mHealth program(s) (aka connected care) should and now can actually guide the end user through a series of self-experiments directed by data-driven feedback from a version of their digital twin. As treatment development and testing moves towards the precision of individual differences inherent in every person and every treatment response (or non-response) group data and more recent big data approaches for generating new knowledge offer limited help to end-users (including practitioners) for helping individuals evaluate their own digital twin generated data and change over time under different conditions. This is the renaissance of N-of-1 or individual science. N-of-1 evaluation creates the opportunity to evaluate each individual uniquely. The rigor and logic of N-of-1 designs have been well articulated and expanded upon for over a half-century. For the clinician, this revitalized form of scientific and behavioral interaction evaluation can help validate or reject the impact a given treatment has for a given patient with increased efficiency and accuracy. Further, N-of-1 can incorporate biological (genomic), behavioral, psychological and digital health data such that users themselves can begin to evaluate the relationships of their own treatment response patterns and the contingencies that impact them. Thus, emerges the self-scientist.

/pdf/digital-twins-and-the-emerging-science-of-self-implications-dbsfyhjbgv.pdf

Digital Twins and the Emerging Science of Self: Implications for Digital Health Experience Design and "Small" Data.

Randomised clinical trials are the preferred method for establishing average intervention effects for groups. Using key methodological elements of these trials, n-of-1 trials provide rigorous evidence of intervention effects for individuals. N-of-1 trials are particularly useful for situations where randomised clinical trials are not always feasible or appropriate, such as for individuals with rare diseases, comorbid conditions, or using concurrent treatments. N-of-1 trials enhance precision when intervention effects are heterogeneous between individuals. Here, we describe an extension to the SPIRIT (standard protocol items: recommendations for interventional trials) guideline, SPENT (SPIRIT extension for n-of-1 trials), to improve the completeness and transparency of n-of-1 trial protocols. SPENT is also aligned with the CONSORT (consolidated standards of reporting trials) extension for n-of-1 trials (CENT). The guideline development group followed the development strategy for reporting guidelines endorsed by the EQUATOR Network. SPENT began with a systematic review for n-of-1 protocol recommendations. After analysis to identify possible SPENT items, a three round Delphi process was implemented, with international participation involving researchers, patient advocates, and other stakeholders. This was followed by in-person meetings and email discussion of the SPENT group to achieve consensus. SPENT has 14 extension items specific to n-of-1 trials, a checklist for n-of-1 trial protocol abstracts, and additional guidance for eight SPIRIT items where trialists could encounter issues specific to n-of-1 trials. This paper describes the rationale and development process, and provides examples and explanations for each SPENT checklist item.

https://www.bmj.com/content/bmj/368/bmj.m122.full.pdf

SPIRIT extension and elaboration for n-of-1 trials: SPENT 2019 checklist

With the objective of increasing the magnitude of treatment effects in behavioral health, there is steadily growing interest in tailoring assessments and interventions to better match individual needs. This aligns with the central idea that behavior can be adequately understood by considering the unique characteristics of the individual and context. Thus, data collected at an individual level provides critical evidence that can be used to inform health care decisions, improve treatment, or refine theories. Yet, the majority of research in behavioral health is based on group-level analyses. Recent developments in the field of single-case experimental design (SCED) has provided new opportunities to utilize individual data. The present article provides a state-of-the art overview regarding key aspects of SCED, including a historical background to why and how SCED emerged, declined, and recently reemerged as well as methodological aspects such as design issues, challenges related to reliability and validity of repeated observations, innovations in visual and statistical analyses of individual data, strategies to deal with missing values, methodology to examine effect size, and approaches to summarize data from a large number of SCEDs using multilevel models and meta-analyses of replication data. Finally, the article discusses key concerns and actions needed to move the field forward.

From Boulder to Stockholm in 70 Years: Single Case Experimental Designs in Clinical Research

‘Trials of therapy’, in which physicians ‘try out’ treatments and assess patients’ responses, are long-established, common elements of routine medical practice. Because ‘trials of therapy’ are usually informal, they may only be reported if treatments are associated with dramatic changes in a patient’s condition – whether by improvement or deterioration. Our understanding of bias suggests that informal ‘trials of therapy’ – comparisons of patients’ condition before and after treatment – do not provide a trustworthy basis for inferring treatment effects. More sophisticated comparisons are usually needed: for example, comparing a patient’s responses when treatments are given or withheld (‘crossed over’) and conducting formal assessment of outcomes. In 1676, Richard Wiseman (a surgeon to King Charles II) reported an unplanned experiment. He had prescribed a pair of laced stockings for a patient suffering from leg oedema. The stockings had reduced the oedema to the extent that the patient ‘was able to walk to his closet, and take the air in his coach, and was well pleased with them’. However, someone suggested to the patient that the stockings might do him harm and persuaded him to remove them. His legs swelled up, he became confined to bed again and developed leg ulcers. Dr Wiseman waited six weeks for the ulcers to heal, restored the laced stockings, with the result that the patient recovered. A century after Wiseman’s crude crossover trial of laced stockings, Caleb Parry, a doctor in Bath, England, published a more formal, planned use of between two and six crossover periods of variable duration in 13 patients, to compare the purgative effects of three varieties of rhubarb. Parry was unable to find any advantage of the more costly Turkish rhubarb compared with English rhubarb. Parry’s ‘trials of therapy’ were important in having used at least two crossovers, but he took no steps to ensure that his andhis patients’ assessments of the treatment effects were not influenced by his or the patients’ knowledgeof the type of rhubarb being given.Fourteen years later, also in Bath, JohnHaygarth compared the effects on rheumatism of a metal ‘tractor’ with a matched wooden (placebo) tractor. This demonstrated that the assumed treatment effects of the metal tractor resulted from patients’ imagination. Haygarth’s study made clear that informal ‘trials of therapy’ can be plagued by false positives (due to placebo effects, physicians’ and patients’ desires to please, the pre-existing expectations of both parties and natural history). And they can also result in false negatives (patients destined to deteriorate and the intervention resulting in them remaining stable). Although more than a century passed after Haygarth before Paul Martini set out principles for designing unbiased crossover trials in his 69-page book, it appears that it was not until 1953 that serious scientific consideration was given to how controlled trials in individual patients could complement traditional parallel group trials. Hogben and Sim recognised that:

https://journals.sagepub.com/doi/pdf/10.1177/0141076817721131

RD Mirza

Papers

The history and development of N-of-1 trials: