Conduct and Implementation of Personalized Trials in Research and Practice

Kravitz, Richard L.; Duan, Naihua

doi:10.1162/99608f92.901255e7

Cited by 7 publications

(9 citation statements)

References 44 publications

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“…The CENT guideline clearly describes a consensus on how to design, analyze, and report N-of-1 trials. 5 The increased availability over time of resources on the conductance of N-of-1 trials such as the CENT guideline and the user's guide on the design and implementation of N-of-1 trials by the Agency for Healthcare Research and Quality 47 is probably largely responsible for the positive correlation found between the quality of reporting scores and a more recent year of publication of the reviewed N-of-1 trials. The higher scores of quality of reporting and design and an N-of-1 trial design with multiple patients could be a consequence of the more systematic and thorough trial planning that is needed for these larger-scale clinical trials.…”

Section: Discussionmentioning

confidence: 99%

N-of-1 Trials in Neurology

et al. 2022

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ObjectiveTo perform a systematic review of published N-of-1 trials (e.g. single patient cross-over trials) in neurological disorders, including an assessment of methodological quality and reporting.MethodsWe searched PubMed, MEDLINE and Embase, from inception date - the first of December 2019, for reports on N-of-1 trials in neurological disorders. Basic trial information on design, disease, intervention, analysis and treatment success was extracted. Strengths and weaknesses of the N-of-1 trials were assessed using the CONSORT extension for N-of-1 trials (CENT) 2015 criteria checklist and the Jadad score as measures of quality and reporting.ResultsWe retrieved 40 reports of N-of-1 trials in neurological disorders (19 individual N-of-1 trials, 21 series of N-of-1 trials). Most N-of-1 trials were performed in neuromuscular and neurodegenerative / movement disorders. Unlike the majority of trials that studied the main symptom(s) of a chronic stable condition, nine N-of-1 trials studied a stable chronic symptom of a progressive or acute neurological disorder. Besides pharmacological interventions, electrical stimulation protocols and nutritional products were studied. A mean total CENT score of 20.88 (SD, 9.10; range 0-43) and mean total Jadad score of 2.90 (SD, 2.15; range 0-5) were found as methodological measures of quality and reporting across all N-of-1 trialsConclusionsN-of-1 trials have been reported in numerous neurological disorders, not only in chronic stable disorders, but also in progressive or acute disorders with a stable symptom. This indicates the emerging therapeutic area of N-of-1 trials in Neurology.Methodological quality and reporting of N-of-1 trials were found suboptimal and can easily be improved in future trials by appropriately describing the methods of blinding and randomization and follow CENT guidelines. As most N-of-1 trials remain unreported in medical literature, this systematic review probably only represent the tip of the iceberg of conducted N-of-1 trials in neurological disorders. In addition to conventional trial designs, N-of-1 trials can help to bridge the gap between research and clinical care by providing an alternative, personalized level 1 evidence-base for suitable treatments.

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Section: Discussionmentioning

confidence: 99%

N-of-1 Trials in Neurology

et al. 2022

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“…are made while the target individual is receiving, and not receiving, an intervention. This contrast between measures while on and off the intervention can then be exploited to quantify and characterize the individual’s response to the intervention but only if enough reliable measurements are made during each of the intervention periods and data analysis methods are used to control for confounding due to, for example, placebo or unmeasured covariate effects (Lillie et al, 2011 ; Kravitz et al, 2014 ; Wang and Schork, 2019 ; Kravitz and Duan, 2022 ). Note that many of the most widely used strategies for avoiding confounding in standard RCTs can be exploited in the design and execution of N-of-1 trials, such as randomizing the order in which the interventions are provided, blinding of the received interventions to the participants and/or researchers analyzing the data, washout periods to avoid carryover effects, etc.…”

Section: Basic N-of-1 Trial Designsmentioning

confidence: 99%

“…Note that many of the most widely used strategies for avoiding confounding in standard RCTs can be exploited in the design and execution of N-of-1 trials, such as randomizing the order in which the interventions are provided, blinding of the received interventions to the participants and/or researchers analyzing the data, washout periods to avoid carryover effects, etc. (Duan, Kravitz, and Schmid 2013;Duan et al 2022;Kravitz and Duan 2022;Kravitz, Duan, and Panel 2014;Lillie et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Exploring human biology with N-of-1 clinical trials

Schork

Beaulieu‐Jones

Liang³

et al. 2023

Camb. prisms Precis. med.

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Studies on humans that exploit contemporary data-intensive, high-throughput 'omic' assay technologies, such as genomics, transcriptomics, proteomics, and metabolomics, have unequivocally revealed that humans differ greatly at the molecular level. These differences, which are compounded by each individual's distinct behavioral and environmental exposures, impact individual responses to health interventions such as diet and drugs. Questions about the best way to tailor health interventions to individuals based on their nuanced genomic, physiologic, behavioral, etc. profiles have motivated the current emphasis on 'precision' medicine. This review's purpose is to describe how the design and execution of N-of-1 (or personalized) multivariate clinical trials can advance the field. Such trials focus on individual responses to health interventions from a whole person perspective, leverage emerging health monitoring technologies, and can be used to address the most relevant questions in the precision medicine era. This includes how to validate biomarkers that may indicate appropriate activity of an intervention as well as how to identify likely beneficial interventions for an individual. We also argue that multivariate N-of-1 and aggregated N-of-1 trials are ideal vehicles for advancing biomedical and translational science in the precision medicine era since the insights gained from them can not only shed light on how to treat or prevent diseases generally, but also provide insight into how to provide real time care to the very individuals who are seeking attention for their health concerns in the first place.

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“…Further details about personalized trials are given in Cheung and Mitsumoto (2022), Davidson et al (2021), Duan et al (2013), Kravitz et al (2014), Kravitz and Duan (2022), Schmid and Yang (2021), and other articles in this special issue.…”

Section: Personalized Trials To Evaluate Treatment Effectivenessmentioning

confidence: 99%

Personalized Data Science and Personalized (N-of-1) Trials: Promising Paradigms for Individualized Health Care

Duan

Norman

Schmid

et al. 2022

Harvard Data Science Review

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The term 'data science' usually refers to the process of extracting value from big data obtained from a large group of individuals. An alternative rendition, which we call personalized data science (Per-DS), aims to collect, analyze, and interpret personal data to inform personal decisions. This article describes the main features of Per-DS, and reviews its current state and future outlook. A Per-DS investigation is of, by, and for an individual, the Per-DS investigator, acting simultaneously as her own investigator, study participant, and beneficiary, and making personalized decisions for study design and implementation. The scope of Per-DS studies may include systematic monitoring of physiological or behavioral patterns, case-crossover studies for symptom triggers, pre-post trials for exposure-outcome relationships, and personalized (N-of-1) trials for effectiveness. Per-DS studies produce personal knowledge generalizable to the individual's future self (thus benefiting herself) rather than knowledge generalizable to an external population (thus benefiting others). This endeavor requires a pivot from data mining or extraction to data gardening, analogous to home gardeners producing food for home consumption-the Per-DS investigator needs to 'cultivate the field' by setting goals, specifying study design, identifying necessary data elements, and assembling instruments and tools for data collection. Then, she can implement the study protocol, harvest her personal data, and mine the data to extract personal knowledge. To facilitate Per-DS studies, Per-DS investigators need support from community-based, scientific, philanthropic, business, and government entities, to develop and deploy resources such as peer forums, mobile apps, 'virtual field guides,' and scientific and regulatory guidance.

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Conduct and Implementation of Personalized Trials in Research and Practice

Cited by 7 publications

References 44 publications

N-of-1 Trials in Neurology

N-of-1 Trials in Neurology

Exploring human biology with N-of-1 clinical trials

Personalized Data Science and Personalized (N-of-1) Trials: Promising Paradigms for Individualized Health Care

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