A methodological review of randomised n-of-1 trials.

BACKGROUND: n-of-1 trials are a type of crossover trial designed to optimise the evaluation of health technologies in individual patients. This trial design may be considered for the evaluation of health technologies in rare conditions where fewer patients are available to take part in research. This review describes the characteristics of randomised n-of-1 trials conducted over the span of 12 years, including how the n-of-1 design has been employed to study both rare and non-rare conditions. METHODS: Databases and clinical trials registries were searched for articles including "n-of-1" in the title between 2011 and 2023. The reference lists of reviews identified by the searches were searched for any additional eligible articles. Randomised n-of-1 trials were selected for inclusion and data were extracted on a range of design, population, and analysis characteristics. Descriptive statistics were produced for all variables. RESULTS: We identified 74 studies meeting our eligibility criteria, 13 of which (17.6%) were conducted in rare conditions. They were conducted in a range of clinical areas with the most common being neurological conditions (n = 16, 21.6%). The median (Q1, Q3) number of participants randomised was 9 (4, 20) and 12 trials (16.2%) involved a single patient only. Forty-six (62.2%) trials evaluated pharmaceutical interventions and 49 (66.2%) trials were placebo controlled. Trials had a median (Q1, Q3) of six (4, 8) periods and 61 (82.4%) compared two health technologies. Fifty-seven (77.0%) trials incorporated blinding and 32 (43.2%) had a washout period. Forty-nine trials (66.2%) used patient-reported outcome measures (PROMs) to assess the primary outcome. Trials used a range of approaches to analysis and 48 (64.9%) combined data from multiple patients. The characteristics of the n-of-1 trials conducted in rare conditions were generally consistent with those in non-rare conditions. CONCLUSIONS: n-of-1 trials are still underused and the application of the n-of-1 design for the evaluation of health technologies for rare diseases has been particularly limited. We have summarised the characteristics of randomised n-of-1 trials in rare and non-rare conditions. We hope that it can inform researchers in the design of future n-of-1 studies. Further work is required to provide guidance on specific design considerations, implementation, and statistical analysis of these studies. TRIAL REGISTRATION: Not applicable.

Analyzing population-level trials as N-of-1 trials: An application to gait.

Studying individual causal effects of health interventions is important whenever intervention effects are heterogeneous between study participants. Conducting N-of-1 trials, which are single-person randomized controlled trials, is the gold standard for their analysis. As an alternative method, we propose to re-analyze existing population-level studies as N-of-1 trials, and use gait as a use case for illustration. Gait data were collected from 16 young and healthy participants under fatigued and non-fatigued, as well as under single-task (only walking) and dual-task (walking while performing a cognitive task) conditions. As a reference to the N-of-1 trials approach, we first computed standard population-level ANOVA models to evaluate differences in gait parameters (stride length and stride time) across conditions. Then, we estimated the effect of the interventions on gait parameters on the individual level through Bayesian repeated-measures models, viewing each participant as their own trial, and compared the results. The results illustrated that while few overall population-level effects were visible, individual-level analyses revealed differences between participants. Baseline values of the gait parameters varied largely among all participants, and the effects of fatigue and cognitive task were also heterogeneous, with some individuals showing effects in opposite directions. These differences between population-level and individual-level analyses were more pronounced for the fatigue intervention compared to the cognitive task intervention. Following our empirical analysis, we discuss re-analyzing population studies through the lens of N-of-1 trials more generally and highlight important considerations and requirements. Our work encourages future studies to investigate individual effects using population-level data.

A qualitative study on the facilitators and barriers to adopting the N-of-1 trial methodology as part of clinical practice: potential versus implementation challenges.

PURPOSE: To investigate opinions among healthcare stakeholders whether implementation of the N-of-1 trial approach in clinical practice is a feasible way to optimize evidence-based treatment results for unique patients. METHODS: We interviewed clinicians, researchers, and a patient advocate (n = 13) with an interest in or experience with N-of-1 trials on the following topics: experience with N-of-1, measurement, validity and reliability, informally gathered data usability, and influence on physician-patient relationship. Interviews were analysed using qualitative, thematic analysis. RESULTS: The N-of-1 approach has the potential to shift the current healthcare system towards embracing personalized medicine. However, its application in clinical practice carries significant challenges in terms of logistics, time investment and acceptability. New skills will be required from patients and healthcare providers, which may alter the patient-physician relationship. The rise of consumer technology enabling self-measurement may leverage the uptake of N-of-1 approaches in clinical practice. CONCLUSIONS: There is a strong belief that the N-of-1 approach has the potential to play a prominent role in transitioning the current healthcare system towards embracing personalized medicine. However, there are many barriers deeply ingrained in our healthcare system that hamper the uptake of the N-of-1 approach, making it momentarily only interesting for research purposes.

Key findings The potential merits of adopting N-of-1 trials into clinical practice (in terms of efficacy and participation) was acknowledged by all participants.The trade-off between methodological rigidity and practical application for the patient was mentioned by clinicians as an important barrier for the use of N-of-1 trials in clinical practice.There appears to be substantial dissensus on the usefulness of "informal/pragmatic" N-of-1 trials in clinical practice; clinicians appear the strongest advocates for strict methodological rigour.What this adds to what is known Previous research suggests that lack of knowledge by researchers, clinicians, and patients on the topic of N-of-1, operational complexity, and costs are primary barriers for adoption of N-of-1 trials in clinical practice.Our work confirms the abovementioned barriers and adds to this list: the current design of the healthcare system and the lack of consensus on methodological requirements.The Quantified Self movement as well as the advances in the wearable technology were mentioned by (patient)researchers as facilitators for the adoption of N-of-1 methodologies in clinical practice.What is the implication, what should change now Education on N-of-1 trials need to be included in the medical (and thus not only the biomedical sciences) curriculum.The N-of-1 approach might help promote shared decision making in which patient can lead using their own data.Best practices of N-of-1 adoption in clinical practice need to be identified and used as examples to further inform communication between medical stakeholders and policymakers.

N-of-1 Trials of Antimicrobial Stewardship Interventions to Optimize Antibiotic Prescribing for Upper Respiratory Tract Infection in Emergency Departments: Protocol for a Quasi-Experimental Study.

BACKGROUND: Antimicrobial stewardship programs attempting to optimize antibiotic therapy and clinical outcomes mainly focus on inpatient and outpatient settings. The lack of antimicrobial stewardship program studies in the emergency department (ED) represents a gap in tackling the problem of antimicrobial resistance as EDs treat a substantial number of upper respiratory tract infection cases throughout the year. OBJECTIVE: We intend to implement two evidence-based interventions: (1) patient education and (2) providing physician feedback on their prescribing rates. We will incorporate evidence from a literature review and contextualizing the interventions based on findings from a local qualitative study. METHODS: Our study uses a quasi-experimental design to evaluate the effects of interventions over time in the EDs of 4 public hospitals in Singapore. We will include an initial control period of 18 months. In the next 6 months, we will randomize 2 EDs to receive 1 intervention (ie, patient education) and the other 2 EDs to receive the alternative intervention (ie, physician feedback). All EDs will receive the second intervention in the subsequent 6 months on top of the ongoing intervention. Data will be collected for another 6 months to assess the persistence of the intervention effects. The information leaflets will be handed to patients at the EDs before they consult with the physician, while feedback to individual physicians by senior doctors is in the form of electronic text messages. The feedback will contain the physicians' antibiotic prescribing rate compared with the departments' overall antibiotic prescribing rate and a bite-size message on good antibiotic prescribing practices. RESULTS: We will analyze the data using segmented regression with difference-in-difference estimation to account for concurrent cluster comparisons. CONCLUSIONS: Our proposed study assesses the effectiveness of evidence-based, context-specific interventions to optimize antibiotic prescribing in EDs. These interventions are aligned with Singapore's national effort to tackle antimicrobial resistance and can be scaled up if successful. TRIAL REGISTRATION: ClinicalTrials.gov NCT05451863; https://clinicaltrials.gov/study/NCT05451836. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50417.

[Cystic fibrosis - ETI and type 2 N-of-1 trials : the next step]. / Mucoviscidose : valider un traitement révolutionnaire par de brefs essais thérapeutiques individuels bien supervisés.

In evidence-based medicine, N-of-1 trials are increasingly attractive for rare and heterogeneous conditions. A recent French study illustrates this convincingly in the field of cystic fibrosis. A highly effective triple therapy (ETI) is currently available in Europe, which will eventually help the 85 % of Belgian patients carrying at least one copy of the F508del mutation. Most other 2.000 or so putative mutations of this gene are poorly characterised and very rare or private. To predict the efficacy of ETI at the individual level in currently ineligible patients, sophisticated tools are advocated, but they are expensive, not widely available, often partially standardised and there still remains a «grey area¼ concerning their reliability in this context. With-out using them, the French study suggests that more than half of these patients show clinically meaningful responses to a 4-6 weeks trial of ETI. What makes this pragmatic, cost-effective, non-invasive and simplified approach possible (type 2 N-of-1 trials) is the dramatic and rapid efficacy of a life-saving treatment without alternative and the fact that it can be assessed using simple and robust clinical and paraclinical outcomes. Here, we describe one such trial and discuss the value and limitations of this approach.

Dans la médecine basée sur les preuves, les essais de taille 1 suscitent un intérêt croissant dans les affections rares et hétérogènes. Une récente étude française l'illustre de manière convaincante dans la mucoviscidose. Une trithérapie extrêmement efficace (ETI) est actuellement disponible en Europe, concernant à terme en Belgique les 85 % de patients porteurs d'au moins une copie de la mutation F508del. La majorité des quelque 2.000 autres mutations putatives de ce gène sont mal caractérisées et rarissimes. Des techniques sophistiquées sont évoquées pour prédire, à l'échelle individuelle, l'efficacité d'ETI chez les patients actuellement non éligibles, mais elles sont peu disponibles, coûteuses, souvent imparfaitement standardisées et leur interprétabilité conserve une «zone grise¼. Sans y recourir, l'étude française montre que plus de la moitié de ces patients répondent d'une manière évidente à un essai d'ETI pendant quelques semaines seulement. Ce qui permet cette approche pragmatique, économique, non invasive et simplifiée (essai de taille 1, de type 2), c'est l'efficacité spectaculaire et rapide d'un traitement salvateur sans alternative et le fait qu'elle puisse être appréhendée à partir de critères cliniques et paracliniques simples et robustes. Nous rapportons ici un essai de ce type et discutons l'intérêt et les limites de cette approche.

Comparison of Bayesian Networks, G-estimation and linear models to estimate causal treatment effects in aggregated N-of-1 trials with carry-over effects.

BACKGROUND: The aggregation of a series of N-of-1 trials presents an innovative and efficient study design, as an alternative to traditional randomized clinical trials. Challenges for the statistical analysis arise when there is carry-over or complex dependencies of the treatment effect of interest. METHODS: In this study, we evaluate and compare methods for the analysis of aggregated N-of-1 trials in different scenarios with carry-over and complex dependencies of treatment effects on covariates. For this, we simulate data of a series of N-of-1 trials for Chronic Nonspecific Low Back Pain based on assumed causal relationships parameterized by directed acyclic graphs. In addition to existing statistical methods such as regression models, Bayesian Networks, and G-estimation, we introduce a carry-over adjusted parametric model (COAPM). RESULTS: The results show that all evaluated existing models have a good performance when there is no carry-over and no treatment dependence. When there is carry-over, COAPM yields unbiased and more efficient estimates while all other methods show some bias in the estimation. When there is known treatment dependence, all approaches that are capable to model it yield unbiased estimates. Finally, the efficiency of all methods decreases slightly when there are missing values, and the bias in the estimates can also increase. CONCLUSIONS: This study presents a systematic evaluation of existing and novel approaches for the statistical analysis of a series of N-of-1 trials. We derive practical recommendations which methods may be best in which scenarios.

N-of-1 trials: The epitome of personalized medicine?

Observational studies are notoriously susceptible to bias, and parallel-group randomized trials are important to identify the best overall treatment for eligible patients. Yet, such trials can be expected to be a misleading indicator of the best treatment for some subgroups or individual patients. In selected circumstances, patients can be treated in n-of-1 trials to address the inherent heterogeneity of treatment response in clinical populations. Such trials help to accomplish the ultimate goal of all biomedical research, to optimize the care of individual patients.

Randomized double-blind personalized N-of-1 clinical trial to test the safety and potential efficacy of TJ-68 for treating muscle cramps in amyotrophic lateral sclerosis (ALS): study protocol for a TJ-68 trial.

INTRODUCTION/AIMS: Muscle cramps are a common and often disabling symptom in amyotrophic lateral sclerosis (ALS), a devastating and incurable neurodegenerative disorder. To date, there are no medications specifically approved for the treatment of muscle cramps. Ameliorating muscle cramps in ALS may improve and sustain quality of life. A widely prescribed traditional Japanese (Kampo) medicine against muscle cramps, shakuyakukanzoto (TJ-68), has been studied in advanced liver disease, spinal stenosis, kidney failure, and diabetic neuropathy. The Japanese ALS Management Guideline mentions TJ-68 for difficult muscle cramps in ALS. Therefore, the rationale of our trial is to investigate the safety and effectiveness of TJ-68 in treating painful and disabling muscle cramps in people with ALS outside of Japan. Accordingly, we are conducting a randomized clinical trial to test the safety and efficacy of TJ-68 in participants with ALS reporting frequent muscle cramps using an innovative, personalized N-of-1 design. If successful, TJ-68 may be used for muscle cramps in a broader population of people with ALS. METHODS: This is a two-site, double-blind, randomized personalized N-of-1 early clinical trial with TJ-68. At least 22 participants with ALS and daily muscle cramps will receive drug or placebo for 2 weeks (one treatment period) followed by a 1-week washout in a four-period cross-over design. While the primary objective is to evaluate the safety of TJ-68, the study has 85% power to detect a one-point shift on the Visual Analog Scale for Muscle Cramps Affecting Overall Daily Activity of the Columbia Muscle Cramp Scale (MCS). Secondary outcomes include the full MCS score, a Cramp Diary, Clinical Global Impression of Changes, Goal Attainment Scale, quality of life scale and ALS functional rating scale-revised (ALSFRS-R). DISCUSSION: The study is underway. A personalized N-of-1 trial design is an efficient approach to testing medications that alleviate muscle cramps in rare disorders. If TJ-68 proves safe and efficacious then it may be used to treat cramps in ALS, and help to improve and sustain quality of life. TRIAL REGISTRATION: This clinical trial has been registered with ClinicalTrials.gov (NCT04998305), 8/9/2021.

LIFEHOUSE's Functional Nutrition Examination (Physical Exam, Anthropometrics, and Selected Biomarkers) Informs Personalized Wellness Interventions.

Each individual has a unique and interacting set of genetic, lifestyle, and environmental factors that are reflected in their physical exam and laboratory biomarkers and significantly impact their experience of health. Patterns of nutrient deficiency signs and biomarker levels below health-promoting thresholds have been identified in national nutrition surveys. However, identifying these patterns remains a challenge in clinical medicine for many reasons, including clinician training and education, clinical time restraints, and the belief that these signs are both rare and recognizable only in cases of severe nutritional deficiencies. With an increased interest in prevention and limited resources for comprehensive diagnostic evaluations, a functional nutrition evaluation may augment patient-centered screening evaluations and personalized wellness programs. During LIFEHOUSE, we have documented physical exam, anthropometric, and biomarker findings that may increase the recognition of these wellness-challenging patterns in a population of 369 adult employees working in two occupational areas: administrative/sales and manufacturing/warehouse. Distinct and significant physical exam differences and constellations of biomarker abnormalities were identified. We present these patterns of physical exam findings, anthropometrics, and advanced biomarkers to assist clinicians in diagnostic and therapeutic interventions that may stem the loss of function that precedes the development of the non-communicable chronic diseases of aging.

Analysis of N-of-1 trials using Bayesian distributed lag model with autocorrelated errors.

An N-of-1 trial is a multi-period crossover trial performed in a single individual, with a primary goal to estimate treatment effect on the individual instead of population-level mean responses. As in a conventional crossover trial, it is critical to understand carryover effects of the treatment in an N-of-1 trial, especially when no washout periods between treatment periods are instituted to reduce trial duration. To deal with this issue in situations where a high volume of measurements are made during the study, we introduce a novel Bayesian distributed lag model that facilitates the estimation of carryover effects, while accounting for temporal correlations using an autoregressive model. Specifically, we propose a prior variance-covariance structure on the lag coefficients to address collinearity caused by the fact that treatment exposures are typically identical on successive days. A connection between the proposed Bayesian model and penalized regression is noted. Simulation results demonstrate that the proposed model substantially reduces the root mean squared error in the estimation of carryover effects and immediate effects when compared to other existing methods, while being comparable in the estimation of the total effects. We also apply the proposed method to assess the extent of carryover effects of light therapies in relieving depressive symptoms in cancer survivors.

Individualized Net Benefit estimation and meta-analysis using generalized pairwise comparisons in N-of-1 trials.

BACKGROUND: The Net Benefit (Δ) is a measure of the benefit-risk balance in clinical trials, based on generalized pairwise comparisons (GPC) using several prioritized outcomes and thresholds of clinical relevance. We extended Δ to N-of-1 trials, with a focus on patient-level and population-level Δ. METHODS: We developed a Δ estimator at the individual level as an extension of the stratum-specific Δ, and at the population-level as an extension of the stratified Δ. We performed a simulation study mimicking PROFIL, a series of 38 N-of-1 trials testing sildenafil in Raynaud's phenomenon, to assess the power for such an analysis with realistic data. We then reanalyzed PROFIL using GPC. This reanalysis was finally interpreted in the context of the main analysis of PROFIL which used Bayesian individual probabilities of efficacy. RESULTS: Simulations under the null showed good size of the test for both individual and population levels. The test lacked power when being simulated from the true PROFIL data, even when increasing the number of repetitions up to 140 days per patient. PROFIL individual-level estimated Δ were well correlated with the probabilities of efficacy from the Bayesian analysis while showing similarly wide confidence intervals. Population-level estimated Δ was not significantly different from zero, consistently with the previous Bayesian analysis. CONCLUSION: GPC can be used to estimate individual Δ which can then be aggregated in a meta-analytic way in N-of-1 trials. GPC ability to easily incorporate patient preferences allow for more personalized treatment evaluation, while needing much less computing time than Bayesian modeling.

An Algorithm to Evaluate Methodological Rigor and Risk of Bias in Single-Case Studies.

Critical appraisal scales play an important role in evaluating methodological rigor (MR) of between-groups and single-case designs (SCDs). For intervention research this forms an essential basis for ascertaining the strength of evidence. Yet, few such scales provide classifications that take into account the differential weighting of items contributing to internal validity. This study aimed to develop an algorithm derived from the Risk of Bias in N-of-1 Trials (RoBiNT) Scale to classify MR and risk of bias magnitude in SCDs. The algorithm was applied to 46 SCD experiments. Two experiments (4%) were classified as Very High MR, 14 (30%) as High, 5 (11%) as Moderate, 2 (4%) as Fair, 2 (4%) as Low, and 21 (46%) as Very Low. These proportions were comparable to the What Works Clearinghouse classifications: 13 (28%) met standards, 8 (17%) met standards with reservations, and 25 (54%) did not meet standards. There was strong association between the two classification systems.

Algorithmic lifestyle optimization.

OBJECTIVE: A hallmark of personalized medicine and nutrition is to identify effective treatment plans at the individual level. Lifestyle interventions (LIs), from diet to exercise, can have a significant effect over time, especially in the case of food intolerances and allergies. The large set of candidate interventions, make it difficult to evaluate which intervention plan would be more favorable for any given individual. In this study, we aimed to develop a method for rapid identification of favorable LIs for a given individual. MATERIALS AND METHODS: We have developed a method, algorithmic lifestyle optimization (ALO), for rapid identification of effective LIs. At its core, a group testing algorithm identifies the effectiveness of each intervention efficiently, within the context of its pertinent group. RESULTS: Evaluations on synthetic and real data show that ALO is robust to noise, data size, and data heterogeneity. Compared to the standard of practice techniques, such as the standard elimination diet (SED), it identifies the effective LIs 58.9%-68.4% faster when used to discover an individual's food intolerances and allergies to 19-56 foods. DISCUSSION: ALO achieves its superior performance by: (1) grouping multiple LIs together optimally from prior statistics, and (2) adapting the groupings of LIs from the individual's subsequent responses. Future extensions to ALO should enable incorporating nutritional constraints. CONCLUSION: ALO provides a new approach for the discovery of effective interventions in nutrition and medicine, leading to better intervention plans faster and with less inconvenience to the patient compared to SED.

The reporting quality of N-of-1 trials and protocols still needs improvement.

OBJECTIVE: To evaluate the reporting quality of single-patient (N-of-1) trials and protocols based on the CONSORT Extension for N-of-1 trials (CENT) statement and the standard protocol items: recommendations for interventional trials (SPIRIT) extension and elaboration for N-of-1 trials (SPENT) checklist to examine the factors that influenced reporting quality. METHODS: Four electronic databases were searched to identify N-of-1 trials and protocols from 2015 to 2020. Quality was assessed by two reviewers. We calculated the overall scores based on binary responses in which "Yes" was scored as 1 (if the item was fully reported), and "No" was scored as 0 (if the item was not clearly reported or not definitely stated). RESULTS: A total of 78 publications (55 N-of-1 trials and 23 protocols) were identified. The mean reporting score (SD) of the N-of-1 trials and protocols were 29.24 (0.89) and 29.61 (1.83), respectively. For the items related to outcomes, sample size, allocation concealment protocol, and informed consent materials, the reporting quality was low. Our results showed that the year of publication (t = -0.793, p = 0.872 for the trials and t = 1.352, p = 0.623 for the protocols) and the impact factor of the journal (t = 1.416, p = 0.619 for the trials and t = 0.359, p = 0.667 for the protocols) were not factors associated with better reporting quality. CONCLUSION: With the publication of the CENT 2015 statement and the SPENT 2019 checklist, authors should adhere to the relevant reporting guidelines and improve the reporting quality of N-of-1 trials and protocols.

A systematic review of N-of-1 trials and single case experimental designs in physiotherapy for musculoskeletal conditions.

BACKGROUND: Single Case Experimental Designs (SCEDs) are especially useful for small heterogeneous samples. Their role in evaluation of physiotherapy interventions for musculoskeletal conditions has not been systematically reviewed. OBJECTIVES: Systematically review use, purpose, and outcomes of SCEDs for physiotherapy interventions for musculoskeletal conditions. DATA SOURCES: Electronic databases and grey literature, searched using pre-defined terms. STUDY SELECTION OR ELIGIBILITY CRITERIA: Studies of human participants enrolled in eligible SCEDs (individual or a series). STUDY APPRAISAL AND SYNTHESIS METHODS: We extracted study characteristics, analytic methods and results, synthesising these descriptively. We used RoBiN-T scale to assess risk of bias. RESULTS: We included 19 SCEDs comprising 92 participants, with wide variability in design, methodology, analysis and in conditions and interventions evaluated. 95% of participants responded favourably to the tested intervention. Overall risk of bias was high, due to poor internal validity, especially regarding randomisation, blinding, inter-rater agreement and measurement of treatment adherence. Visual analysis alone was performed in 55% of studies. Assessment of provider and participant satisfaction was limited. CONCLUSIONS AND IMPLICATIONS: of key findings: SCEDs may be well-suited to evaluation of physiotherapy interventions for musculoskeletal conditions, but the risk of bias in studies to date is high. Following SCED guidelines to minimize the risk of bias and maximise clinical usefulness is recommended.

An Argument in Favor of Deep Brain Stimulation for Uncommon Movement Disorders: The Case for N-of-1 Trials in Holmes Tremor.

Deep brain stimulation (DBS) is part of state-of-the-art treatment for medically refractory Parkinson's disease, essential tremor or primary dystonia. However, there are multiple movement disorders that present after a static brain lesion and that are frequently refractory to medical treatment. Using Holmes tremor (HT) as an example, we discuss the effectiveness of currently available treatments and, performing simulations using a Markov Chain approach, propose that DBS with iterative parameter optimization is expected to be more effective than an approach based on sequential trials of pharmacological agents. Since, in DBS studies for HT, the thalamus is a frequently chosen target, using data from previous studies of lesion connectivity mapping in HT, we compared the connectivity of thalamic and non-thalamic targets with a proxy of the HT network, and found a significantly higher connectivity of thalamic DBS targets in HT. The understanding of brain networks provided by analysis of functional connectivity may thus provide an informed framework for proper surgical targeting of individual patients. Based on these findings, we argue that there is an ethical imperative to at least consider surgical options in patients with uncommon movement disorders, while simultaneously providing consistent information regarding the expected effectiveness and risks, even in a scenario of surgical-risk aversion. An approach based on n-of-1 DBS trials may ultimately significantly improve outcomes while informing on optimal therapeutic targets and parameter settings for HT and other disabling and rare movement disorders.

Effectiveness of modified Buzhong Yiqi decoction in treating myasthenia gravis: study protocol for a series of N-of-1 trials.

BACKGROUND: Myasthenia gravis (MG) is an acquired autoimmune disease with high heterogeneity. The disease is chronic, relapsing repeatedly and progressive with acute exacerbation occasionally. Although the treatment of MG has developed, it is still unsatisfactory and has some unexpected side effects. Traditional Chinese medicine (TCM) has shown great potential in MG treatment, including relief of muscle weakness syndrome, improvement of patient's quality of life, and reduction of side effects of western medicine. The purpose of this study is to evaluate the effectiveness of modified Buzhong Yiqi decoction (MBYD) as an add-on therapy for MG through a small series of N-of-1 trials. METHODS: Single-centre, randomized, double-blind, 3 crossover N-of-1 trials will be conducted to enroll patients with MG diagnosed as spleen-stomach deficiency syndrome or spleen-kidney deficiency syndrome in TCM. Each N-of-1 trial has 3 cycles of two 4-week periods containing the MBYD period and placebo period. The wash-out interval of 1 week is prior to switching each period. PRIMARY OUTCOME: quantitative myasthenia gravis (QMG). SECONDARY OUTCOMES: the following scales: myasthenia gravis composite (MGC), myasthenia gravis activities of daily living profile (MG-ADL), myasthenia gravis quality of life (MG-QOL); the level of CD4+FoxP3+Treg cells and cytokines (IL-4, IL-17A, INF-γ, TGF-ß) in the peripheral blood; the alterations of the composition of gut microbiota; reduction of the side effects of western medicine. DISCUSSION: Used by WinBUGS software, we will conduct a hierarchical Bayesian statistical method to analyze the efficacy of MBYD in treating MG in individuals and populations. Some confounding variables such as TCM syndrome type and potential carryover effect of TCM will be introduced into the hierarchical Bayesian statistical method to improve the sensitivity and applicability of the trials, and the use of prior available information within the analysis may improve the sensitivity of the results of a series of N-of-1 trials, from both the individual and population level to study the efficacy of TCM syndrome differentiation. We assumed that this study would reveal that MBYD is effective for MG and provide robust evidence of the efficacy of TCM to treat MG. TRIAL REGISTRATION: Chinese Clinical Trial Register, ID: ChiCTR2000040477 , registration on 29 November 2020.

Personalized (N-of-1) Trials for Patient-Centered Treatments of Multimorbidity.

Treatment of patients who suffer from concurrent health conditions is not well served by (1) evidence-based clinical guidelines that mainly specify treatment of single conditions and (2) conventional randomized controlled trials (RCTs) that identify treatments as safe and effective on average. Clinical decision-making based on the average patient effect may be inappropriate for treatment of those with multimorbidity who experience burdens and obstacles that may be unique to their personal situation. We describe how the personalized (N-of-1) trials can be integrated with an automatic platform and virtual/remote technologies to improve patient-centered care for those living with multimorbidity. To illustrate, we present a hypothetical clinical scenario-survivors of both coronavirus disease 2019 (COVID-19) and cancer who chronically suffer from sleeplessness and fatigue. Then, we will describe how the four standard phases of conventional RCT development can be modified for personalized trials and applied to the multimorbidity clinical scenario, outline how personalized trials can be adapted and extended to compare the benefits of personalized trials versus between-subject trial design, and explain how personalized trials can address special problems associated with multimorbidity for which conventional trials are poorly suited.

Introducing Data Sciences to N-of-1 Designs, Statistics, Use-Cases, the Future, and the Moniker 'N-of-1' Trial.

This article, an introduction to HDSR's "Personalized (N-of-1) Trials: Methods, Applications, and Impact" special issue, describes the rationale for a primer of the methods, data types and management, designs, and use cases for personalized (N-of-1) trials. It explains that the design and implementation of personalized (N-of-1) trials is only useful if patients volunteer for research involving them and actively participate in clinical services that use them. However, 'N-of-1 trials' may be an inadequate name to enact such patient engagement. The authors briefly review what patients have reported about the 'N-of-1' label and propose a more consumer-friendly moniker for this type of research and clinical approach to improve evidence-based science.

N-of-1 Trials, Their Reporting Guidelines, and the Advancement of Open Science Principles.

N-of-1 trials are multiple crossover trials done over time within a single person; they can also be done with a series of individuals. Their focus on the individual as the unit of analysis maintains statistical power while accommodating greater differences between patients than most standard clinical trials. This makes them particularly useful in rare diseases, while also being applicable across many health conditions and populations. Best practices recommend the use of reporting guidelines to publish research in a standardized and transparent fashion. N-of-1 trials have the SPIRIT extension for N-of-1 protocols (SPENT) and the CONSORT extension for N-of-1 trials (CENT). Open science is a recent movement focused on making scientific knowledge fully available to anyone, increasing collaboration, and sharing of scientific efforts. Open science goals increase research transparency, rigor, and reproducibility, and reduce research waste. Many organizations and articles focus on specific aspects of open science, for example, open access publishing. Throughout the trajectory of research (idea, development, running a trial, analysis, publication, dissemination, knowledge translation/reflection), many open science ideals are addressed by the individual-focused nature of N-of-1 trials, including issues such as patient perspectives in research development, personalization, and publications, enhanced equity from the broader inclusion criteria possible, and easier remote trials options. However, N-of-1 trials also help us understand areas of caution, such as monitoring of post hoc analyses and the nuances of confidentiality for rare diseases in open data sharing. The N-of-1 reporting guidelines encourage rigor and transparency of N-of-1 considerations for key aspects of the research trajectory.