Individual (N-of-1) trials can be combined to give population comparative treatment effect estimates: methodologic considerations

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.

Rush to Charge, Dead to Drive: Application of Deadline Rush Model to Electric Vehicle User's Charging Behavior

OBJECTIVE

This work aims to estimate the portion of electric vehicle (EV) users who exhibit procrastination-like behavior, almost equivalent to an "empty" battery, before they decide to charge their vehicles.

BACKGROUND

There is a human tendency to procrastinate when a deadline approaches. Human behavior in the presence of deadlines has been studied in different fields to evaluate individuals' performance or organizational efficiency and effectiveness. However, this phenomenon has not been investigated among EV users.

METHOD

This study explores users' procrastination-like behavior among 69 Rhode Island public charging stations' data representing 70,611 charging events. The Deadline Rush Model is incorporated to model frequent users' charging profiles. To conduct a robust estimation, the Bayesian Mixture Model is implemented.

RESULTS

With the selection of an informative prior, the Bayesian Mixture Model estimated that almost one-third of frequent users procrastinate charging.

CONCLUSION

The majority of procrastination-like users have small battery sizes. Although procrastination-like users need to charge when they arrive at a location, that might not necessarily be true for a plug-in hybrid; thus, systematically, they can clog the system for other users whose needs are more pressing. Understanding unique and unexplored charging behaviors among EV users is beneficial to EV infrastructure stakeholders in reducing the adoption threshold by providing a reliable and ubiquitous charging network.

APPLICATION

The findings identify a different kind of demand on the EV infrastructure than previously modeled and can directly influence future decision-making criteria in terms of planning to optimize to accommodate EV drivers with different charging behaviors.

The analysis of continuous data from n-of-1 trials using paired cycles: a simple tutorial

N-of-1 trials are defined and the popular paired cycle design is introduced, together with an explanation as to how suitable sequences may be constructed.Various approaches to analysing such trials are explained and illustrated using a simulated data set. It is explained how choosing an appropriate analysis depends on the question one wishes to answer. It is also shown that for a given question, various equivalent approaches to analysis can be found, a fact which may be exploited to expand the possible software routines that may be used.Sets of N-of-1 trials are analogous to sets of parallel group trials. This means that software for carrying out meta-analysis can be used to combine results from N-of-1 trials. In doing so, it is necessary to make one important change, however. Because degrees of freedom for estimating variances for individual subjects will be scarce, it is advisable to estimate local standard errors using pooled variances. How this may be done is explained and fixed and random effect approaches to combining results are illustrated.

Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine

Power analysis informs a priori planning of behavioral and medical research, including for randomized clinical trials that are nomothetic (i.e., studies designed to infer results to the general population based on interindividual variabilities). Far fewer investigations and resources are available for power analysis of clinical trials that follow an idiographic approach, which emphasizes intraindividual variabilities between baseline (control) phase versus one or more treatment phases. We tested the impact on statistical power to detect treatment outcomes of four idiographic trial design factors that are under researchers' control, assuming a multiple baseline design: sample size, number of observations per participant, proportion of observations in the baseline phase, and competing statistical models (i.e., hierarchical modeling versus piecewise regression). We also tested the impact of four factors that are largely outside of researchers' control: population size, proportion of intraindividual variability due to residual error, treatment effect size, and form of outcomes during the treatment phase (phase jump versus gradual change). Monte Carlo simulations using all combinations of the factors were sampled with replacement from finite populations of 200, 1750, and 3500 participants. Analyses characterized the unique relative impact of each factor individually and all two-factor combinations, holding all others constant. Each factor impacted power, with the greatest impact being from larger treatment effect sizes, followed respectively by more observations per participant, larger samples, less residual variance, and the unexpected improvement in power associated with assigning closer to 50% of observations to the baseline phase. This study's techniques and R package better enable a priori rigorous design of idiographic clinical trials for rare diseases, precision medicine, and other small-sample studies.

Personalized redox biology: Designs and concepts

Personalized interventions are regarded as a next-generation approach in almost all fields of biomedicine, such as clinical medicine, exercise, nutrition and pharmacology. At the same time, an increasing body of evidence indicates that redox processes regulate, at least in part, multiple aspects of human physiology and pathology. As a result, the idea of applying personalized redox treatments to improve their efficacy has gained popularity among researchers in recent years. The aim of the present primer-style review was to highlight some crucial yet underappreciated methodological, statistical, and interpretative concepts within the redox biology literature, while also providing a physiology-oriented perspective on personalized redox biology. The topics addressed are: (i) the critical issue of investigating the potential existence of inter-individual variability; (ii) the importance of distinguishing a genuine and consistent response of a subject from a chance finding; (iii) the challenge of accurately quantifying the effect of a redox treatment when dealing with 'extreme' groups due to mathematical coupling and regression to the mean; and (iv) research designs and analyses that have been implemented in other fields, and can be reframed and exploited in a redox biology context.

Study protocol: combined N-of-1 trials to assess open-label placebo treatment for antidepressant discontinuation symptoms [FAB-study]

BACKGROUND

Antidepressant discontinuation is associated with a broad range of adverse effects. Debilitating discontinuation symptoms can impede the discontinuation process and contribute to unnecessary long-term use of antidepressants. Antidepressant trials reveal large placebo effects, indicating a potential use of open-label placebo (OLP) treatment to facilitate the discontinuation process. We aim to determine the effect of OLP treatment in reducing antidepressant discontinuation symptoms using a series of N-of-1 trials.

METHODS

A series of randomized, single-blinded N-of-1 trials will be conducted in 20 patients with fully remitted DSM-V major depressive disorder, experiencing moderate to severe discontinuation symptoms following antidepressant discontinuation. Each N-of-1 trial consists of two cycles, each comprising two-week alternating periods of OLP treatment and of no treatment in a random order, for a total of eight weeks. Our primary outcome will be self-reported discontinuation symptoms rated twice daily via the smartphone application 'StudyU'. Secondary outcomes include expectations about discontinuation symptoms and (depressed) mood. Statistical analyses will be based on a Bayesian multi-level random effects model, reporting posterior estimates of the overall and individual treatment effects.

DISCUSSION

Results of this trial will provide insight into the clinical application of OLP in treating antidepressant discontinuation symptoms, potentially offering a new cost-effective therapeutic tool. This trial will also determine the feasibility and applicability of a series of N-of-1 trials in a clinical discontinuation trial.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT05051995, first registered September 20, 2021.

The use of N-of-1 trials to generate real-world evidence for optimal treatment of individuals and populations

Introduction

Ideally, real-world data (RWD) collected to generate real-world evidence (RWE) should lead to impact on the care and health of real-world patients. Deriving from care in which clinicians and patients try various treatments to inform therapeutic decisions, N-of-1 trials bring scientific methods to real-world practice.

Methods

These single-patient crossover trials generate RWD and RWE by giving individual patients various treatments in a double-blinded way in sequential periods to determine the most effective treatment for a given patient.

Results

This approach is most often used for patients with chronic, relatively stable conditions that provide the opportunity to make comparisons over multiple treatment periods, termed Type 1 N-of-1 trials. These are most helpful when there is heterogeneity of treatment effects among patients and no a priori best option. N-of-1 trials also can be done for patients with rare diseases, potentially testing only one treatment, to generate evidence for personalized treatment decisions, designated as Type 2 N-of-1 trials. With both types, in addition to informing individual's treatments, when uniform protocols are used for multiple patients with the same condition, the data collected in the individual N-of-1 trials can be aggregated to provide RWD/RWE to inform more general use of the treatments. Thereby, N-of-1 trials can provide RWE for the care of individuals and for populations.

Conclusions

To fulfill this potential, we believe N-of-1 trials should be built into our current healthcare ecosystem. To this end, we are building the needed infrastructure and engaging the stakeholders who should receive value from this approach.

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.

Daily socket comfort in transtibial amputee with a vacuum-assisted suspension system: study protocol of a randomized, multicenter, double-blind multiple N-of-1 trial

BACKGROUND

The main aim of this paper is to present the feasibility of rigorously designed multiple N-of-1 design in prosthetics research. While research of adequate power and high quality is often lacking in rehabilitation, N-of-1 trials can offer a feasible alternative to randomized controlled group trials, both increasing design power at group level and allowing a rigorous, statistically confirmed evaluation of effectiveness at a single patient level. The paper presents a multiple N-of-1 trial protocol, which aim is to evaluate the effectiveness of Unity, a prosthetic add-on suspension system for amputees, on patient-reported comfort during daily activities (main outcome measure), prosthesis wearing time, perception of limb-prosthesis fitting and stump volume and functional walking parameters.

METHODS

Multicenter, randomized, prospective, double-blind multiple N-of-1 trial using an introduction/withdrawal design alternating Unity connected/disconnected phases of randomized length on twenty patients with unilateral transtibial amputation. The primary outcome measure is the Prosthetic Socket Comfort Score (SCS), a validated measure of comfort, administered daily by an phone app designed for the study. Secondary outcomes measures will be collected during the 50 days period of the N-of-1 trial: (1) by the same app, daily for patient-reported limb-prosthesis fitting, stump volume variation, and daily wearing time of the prosthesis; (2) by a pedometer for the number of steps per day; (3) by blind assessors in the rehabilitation center during adjustment visits for functional walking parameter (L-Test, 6-minute walk test), and by the patient for the QUEST, and ABC-S. Effectiveness of the Unity system regarding SCS and daily secondary outcome measures will be tested by randomization test. The secondary outcome measures assessed during visits in the rehabilitation center will be analyzed by Non Overlap of All pairs. An estimate of the effect on the amputee population will be generated by aggregating each individual clinical trial (N-of-1 trial) by Hierarchical Bayesian methods.

DISCUSSION

This study protocol was designed to answer the question "which device is best for THIS patient" and to conclude at a group level on the effectiveness of a new devic, using a Multiple N-of-1 trial, which is promising but underused in prosthetics research so far.

TRIAL REGISTRATION

N° ID-RCB 2020-A01309-30 Clintrial.gov : NCT04804150 - Retrospectively registered March 20th 2021.

Research objectives and general considerations for pragmatic clinical trials of pain treatments: IMMPACT statement

ABSTRACT

Many questions regarding the clinical management of people experiencing pain and related health policy decision-making may best be answered by pragmatic controlled trials. To generate clinically relevant and widely applicable findings, such trials aim to reproduce elements of routine clinical care or are embedded within clinical workflows. In contrast with traditional efficacy trials, pragmatic trials are intended to address a broader set of external validity questions critical for stakeholders (clinicians, healthcare leaders, policymakers, insurers, and patients) in considering the adoption and use of evidence-based treatments in daily clinical care. This article summarizes methodological considerations for pragmatic trials, mainly concerning methods of fundamental importance to the internal validity of trials. The relationship between these methods and common pragmatic trials methods and goals is considered, recognizing that the resulting trial designs are highly dependent on the specific research question under investigation. The basis of this statement was an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) systematic review of methods and a consensus meeting. The meeting was organized by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) public-private partnership. The consensus process was informed by expert presentations, panel and consensus discussions, and a preparatory systematic review. In the context of pragmatic trials of pain treatments, we present fundamental considerations for the planning phase of pragmatic trials, including the specification of trial objectives, the selection of adequate designs, and methods to enhance internal validity while maintaining the ability to answer pragmatic research questions.

Exploring human biology with N-of-1 clinical trials

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.

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.

N-of-1 trials to facilitate evidence-based deprescribing: Rationale and case study

Deprescribing has emerged as an important aspect of patient-centred medication management but is vastly underutilized in clinical practice. The current narrative review will describe an innovative patient-centred approach to deprescribing-N-of-1 trials. N-of-1 trials involve multiple-period crossover design experiments conducted within individual patients. They enable patients to compare the effects of two or more treatments or, in the case of deprescribing N-of-1 trials, continuation with a current treatment versus no treatment or placebo. N-of-1 trials are distinct from traditional between-patient studies such as parallel-group or crossover designs which provide an average effect across a group of patients and obscure differences between individuals. By generating data on the effect of an intervention for the individual rather than the population, N-of-1 trials can promote therapeutic precision. N-of-1 trials are a particularly appealing strategy to inform deprescribing because they can generate individual-level evidence for deprescribing when evidence is uncertain, and can thus allay patient and physician concerns about discontinuing medications. To illustrate the use of deprescribing N-of-1 trials, we share a case example of an ongoing series of N-of-1 trials that compare maintenance versus deprescribing of beta-blockers in patients with heart failure with preserved ejection fraction. By providing quantifiable data on patient-reported outcomes, promoting personalized pharmacotherapy, and facilitating shared decision making, N-of-1 trials represent a potentially transformative strategy to address polypharmacy.

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

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.

Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force

Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR. The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation-helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis. Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results. To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.

Personalized Research on Diet in Ulcerative Colitis and Crohn's Disease: A Series of N-of-1 Diet Trials

INTRODUCTION

Evidence about specific carbohydrate diet (SCD) for inflammatory bowel disease (IBD) is limited. We conducted 54 single-subject, double-crossover N-of-1 trials comparing SCD with a modified SCD (MSCD) and comparing each with the participant's baseline, usual diet (UD).

METHODS

Across 19 sites, we recruited patients aged 7-18 years with IBD and active inflammation. Following a 2-week baseline (UD), patients were randomized to 1 of 2 sequences of 4 alternating 8-week SCD and MSCD periods. Outcomes included fecal calprotectin and patient-reported symptoms. We report posterior probabilities from Bayesian models comparing diets.

RESULTS

Twenty-one (39%) participants completed the trial, 9 (17%) completed a single crossover, and 24 (44%) withdrew. Withdrawal or early completion occurred commonly (lack of response [n = 11], adverse events [n = 11], and not desiring to continue [n = 6]). SCD and MSCD performed similarly for most individuals. On average, there was <1% probability of a clinically meaningful difference in IBD symptoms between SCD and MSCD. The average treatment difference was -0.3 (95% credible interval -1.2, 0.75). There was no significant difference in the ratio of fecal calprotectin geometric means comparing SCD and MSCD (0.77, 95% credible interval 0.51, 1.10). Some individuals had improvement in symptoms and fecal calprotectin compared with their UD, whereas others did not.

DISCUSSION

SCD and MSCD did not consistently improve symptoms or inflammation, although some individuals may have benefited. However, there are inherent difficulties in examining dietary changes that complicate study design and ultimately conclusions regarding effectiveness.

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-β) 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.

Evaluating the Effects of Heat-Clearing Traditional Chinese Medicine in Stable Bronchiectasis by a Series of N-of-1 Trials

PURPOSE

The purpose of this study is to study the effects of heat-clearing Traditional Chinese Medicine (TCM) in the stable stage of bronchiectasis via N-of-1 trials.

METHODS

The N-of-1 trials in this study were randomized and double-blinded with crossover comparisons consisting of three pairs. Each pair was of two 4-week periods. Each patient took the individualized decoction in the experimental period and the individualized decoction was removed of heat-clearing drugs, mainly including heat-clearing and detoxifying drugs, in the control period for three weeks. After three weeks, the patients stopped taking the decoction for one week. The primary outcome was from patients' self-reporting symptoms scores on a 1-7-point Likert scale. Mixed-effects models were used to conduct statistical analysis on these N-of-1 trials.

RESULTS

Of the 21 patients enrolled, 15 completed three pairs of N-of-1 trials (71.43%). (1) Seen from the individual level, no statistical difference between the experimental decoction and the control (P > 0.05) was observed. However, 5 patients found better decoctions according to the clinical criteria. (2) As revealed by the group data of all the N-of-1 trials, the control was better than the individualized decoction in terms of symptom scores on the Likert scale (1.94 ± 0.69 versus 2.08 ± 0.68, P = 0.04, mean difference, and 95% CI: 0.19 (0.01, 0.37)) and on CAT scores (13.66 ± 6.57 versus 13.95 ± 6.97, P = 0.04, mean difference, and 95% CI: 0.86 (0.042, 1.67)), but such differences were not clinically significant. The other outcomes, such as Likert scale score of respiratory symptoms and 24-hour sputum volume, showed no statistical difference.

CONCLUSION

The experimental design of this study can make the TCM individualized treatment fully play its role and can detect the individualized tendencies according to the severity of phlegm and heat in some subjects. With the intermittent use or reduced use of heat-clearing drugs, most of the subjects, at the group level, enrolled in the series of N-of-1 trials may improve the symptoms and quality of life while saving the cost of TCM and reducing the potential side effects of heat-clearing TCM. This trial is registered with clinicaltrials.goc (NCT03147443).

The value of real world evidence: The case of medical cannabis

Randomised controlled trials (RCTs) have long been considered the gold standard of medical evidence. In relation to cannabis based medicinal products (CBMPs), this focus on RCTs has led to very restrictive guidelines in the UK, which are limiting patient access. There is general agreement that RCT evidence in relation to CBPMs is insufficient at present. As well as commercial reasons, a major problem is that RCTs do not lend themselves well to the study of whole plant medicines. One solution to this challenge is the use of real world evidence (RWE) with patient reported outcomes (PROs) to widen the evidence base. Such data increasingly highlights the positive impact medical cannabis can have on patients' lives. This paper outlines the value of this approach which involves the study of interventions and patients longitudinally under medical care. In relation to CBMPs, RWE has a broad range of advantages. These include the study of larger groups of patients, the use of a broader range and ratio of components of CBMPs, and the inclusion of more and rarer medical conditions. Importantly, and in contrast to RCTs, patients with significant comorbidities-and from a wider demographic profile-can also be studied, so providing higher ecological validity and increasing patient numbers, whilst offering significant cost savings. We conclude by outlining 12 key recommendations of the value of RWE in relation to medical cannabis. We hope that this paper will help policymakers and prescribers understand the importance of RWE in relation to medical cannabis and help them develop approaches to overcome the current situation which is detrimental to patients.

Can pragmatic research, real-world data and digital technologies aid the development of psychedelic medicine?

Favourable regulatory assessments, liberal policy changes, new research centres and substantial commercial investment signal that psychedelic therapy is making a major comeback. Positive findings from modern trials are catalysing developments, but it is questionable whether current confirmatory trials are sufficient for advancing our understanding of safety and best practice. Here we suggest supplementing traditional confirmatory trials with pragmatic trials, real-world data initiatives and digital health solutions to better support the discovery of optimal and personalised treatment protocols and parameters. These recommendations are intended to help support the development of safe, effective and cost-efficient psychedelic therapy, which, given its history, is vulnerable to excesses of hype and regulation.

N-of-1 Trials in Pediatric Oncology: From a Population-Based Approach to Personalized Medicine-A Review

Pediatric oncology is a critical area where the more efficient development of new treatments is urgently needed. The speed of approval of new drugs is still limited by regulatory requirements and a lack of innovative designs appropriate for trials in children. Childhood cancers meet the criteria of rare diseases. Personalized medicine brings it even closer to the horizon of individual cases. Thus, not all the traditional research tools, such as large-scale RCTs, are always suitable or even applicable, mainly due to limited sample sizes. Small samples and traditional versus subject-specific evidence are both distinctive issues in personalized pediatric oncology. Modern analytical approaches and adaptations of the paradigms of evidence are warranted. We have reviewed innovative trial designs and analytical methods developed for small populations, together with individualized approaches, given their applicability to pediatric oncology. We discuss traditional population-based and individualized perspectives of inferences and evidence, and explain the possibilities of using various methods in pediatric personalized oncology. We find that specific derivatives of the original N-of-1 trial design adapted for pediatric personalized oncology may represent an optimal analytical tool for this area of medicine. We conclude that no particular N-of-1 strategy can provide a solution. Rather, a whole range of approaches is needed to satisfy the new inferential and analytical paradigms of modern medicine. We reveal a new view of cancer as continuum model and discuss the "evidence puzzle".

Individualized therapy trials: navigating patient care, research goals and ethics

'Individualized therapy' trials (sometimes called n-of-1 trials) use patients as their own controls to evaluate treatments. Here we divide such trials into three categories: multi-crossover trials aimed at individual patient management, multi-crossover trial series and pre-post trials. These trials all customize interventions for patients; however, the latter two categories also aim to inform medical practice and thus embody tensions between the goals of care and research that are typical of other types of clinical trials. In this Perspective, we discuss four domains where such tensions play out-clinical equipoise, informed consent, reporting and funding, and we provide recommendations for addressing each.

Individual Postprandial Glycemic Responses to Diet in n-of-1 Trials: Westlake N-of-1 Trials for Macronutrient Intake (WE-MACNUTR)

BACKGROUND

The role of different types and quantities of macronutrients on human health has been controversial, and the individual response to dietary macronutrient intake needs more investigation.

OBJECTIVES

We aimed to use an 'n-of-1' study design to investigate the individual variability in postprandial glycemic response when eating diets with different macronutrient distributions among apparently healthy adults.

METHODS

Thirty apparently healthy young Chinese adults (women, 68%) aged between 22 and 34 y, with BMI between 17.2 and 31.9 kg/m2, were provided with high-fat, low-carbohydrate (HF-LC, 60-70% fat, 15-25% carbohydrate, 15% protein, of total energy) and low-fat, high-carbohydrate (LF-HC, 10-20% fat, 65-75% carbohydrate, 15% protein) diets, for 6 d wearing continuous glucose monitoring systems, respectively, in a randomized sequence, interspersed by a 6-d wash-out period. Three cycles were conducted. The primary outcomes were the differences of maximum postprandial glucose (MPG), mean amplitude of glycemic excursions (MAGE), and AUC24 between intervention periods of LF-HC and HF-LC diets. A Bayesian model was used to predict responders with the posterior probability of any 1 of the 3 outcomes reaching a clinically meaningful difference.

RESULTS

Twenty-eight participants were included in the analysis. Posterior probability of reaching a clinically meaningful difference of MPG (0.167 mmol/L), MAGE (0.072 mmol/L), and AUC24 (13.889 mmol/L·h) between LF-HC and HF-LC diets varied among participants, and those with posterior probability >80% were identified as high-carbohydrate responders (n = 9) or high-fat responders (n = 6). Analyses of the Bayesian-aggregated n-of-1 trials among all participants showed a relatively low posterior probability of reaching a clinically meaningful difference of the 3 outcomes between LF-HC and HF-LC diets.

CONCLUSIONS

N-of-1 trials are feasible to characterize personal response to dietary intervention in young Chinese adults.

A Useful and Sustainable Role for N-of-1 Trials in the Healthcare Ecosystem

Clinicians and patients often try a treatment for an initial period to inform longer‐term therapeutic decisions. A more rigorous approach involves N‐of‐1 trials. In these single‐patient crossover trials, typically conducted in patients with chronic conditions, individual patients are given candidate treatments in a double‐blinded, random sequence of alternating periods to determine the most effective treatment for that patient. However, to date, these trials are rarely done outside of research settings and have not been integrated into general care where they could offer substantial benefit. Designating this classical, N‐of‐1 trial design as type 1, there also are new and evolving uses of N‐of‐1 trials that we designate as type 2. In these, rather than focusing on optimizing treatment for chronic diseases when multiple approved choices are available, as is typical of type 1, a type 2 N‐of‐1 trial tests treatments designed specifically for a patient with a rare disease, to facilitate personalized medicine. While the aims differ, both types face the challenge of collecting individual‐patient evidence using standard, trusted, widely accepted methods. To fulfill their potential for producing both clinical and research benefits, and to be available for wide use, N‐of‐1 trials will have to fit into the current healthcare ecosystem. This will require generalizable and accepted processes, platforms, methods, and standards. This also will require sustainable value‐based arrangements among key stakeholders. In this article, we review opportunities, stakeholders, issues, and possible approaches that could support general use of N‐of‐1 trials and deliver benefit to patients and the healthcare enterprise. To assess and expand the benefits of N‐of‐1 trials, we propose multistakeholder meetings, workshops, and the generation of methods, standards, and platforms that would support wider availability and the value of N‐of‐1 trials.

Using population crossover trials to improve the decision process regarding treatment individualization in N-of-1 trials

Healthcare researchers are showing renewed interest in the utilization of N-of-1 clinical trials for the individualization of pharmacological treatments. Here, we propose a frequentist approach to conducting treatment individualization in N-of-1 trials that we call "partial empirical Bayes." We infer the most beneficial treatment for the patient from combining the information provided by a previously conducted population crossover trial with individual patient data. We propose a method for estimating an optimal number of treatment cycles and investigate the statistical conditions under which N-of-1 trials are more beneficial than traditional clinical approaches. We represent the patient population with a random-coefficients linear model and calculate estimators of posttreatment individual disease severities. We show the estimators' consistency under the most common N-of-1 designs and examine their prediction errors and performance with small numbers of patient's responses. We demonstrate by simulating new patients that our approach is equivalent or superior to both the common clinical practice of recommending the on-average best treatment for all patients and the common individualization method that simply compares average responses to the tested treatments. We conclude that some situations exist in which individualization with N-of-1 trials is highly beneficial while other situations exist in which individualization may be unfruitful.

N-of-1 Clinical Trials: A Scientific Approach to Personalized Medicine for Patients with Rare Retinal Diseases Such as Retinitis Pigmentosa

N-of-1 trials are randomized, prospective, controlled, multiple crossover trials in a single patient. Effects of one or more treatments are studied by following individual patients who receive alternative treatments (eg, therapeutic intervention). Such trials may provide a path to assess treatments for rare diseases with rigor equal to or greater than that afforded by parallel group randomized clinical trials provided that the condition is reasonably stable during the trial and has a sign/symptom that responds reversibly to the therapy and that can be measured repeatedly. In this article, the authors propose that N-of-1 trials may improve the feasibility and affordability of clinical trials for patients with rare inherited retinal diseases.

Methodological Considerations in N-of-1 Trials of Traditional Chinese Medicine

More and more scholars choose N-of-1 trials for TCM clinical research. However, the quality of the experimental designs was uneven. Accumulating more than eight years of experience in exploring the N-of-1 trials of TCM, the authors and their team searched the related literature in main Chinese and English databases, referenced to relevant Chinese and international guidelines. The design, implementation, and data analysis of N-of-1 trials of TCM are still in in-depth exploration and practice. “Carryover effect” may affect the design and quality of the trials. Individualized treatment should be guided by the classic theories of TCM. It is expected to formulate reasonable observation periods and pairs and closely integrate individual and group statistical analysis.

A Series of N-of-1 Trials for Traditional Chinese Medicine Using a Bayesian Method: Study Rationale and Protocol

Background. Our previous studies showed that N-of-1 trials could reflect the individualized characteristics of traditional Chinese medicine (TCM) syndrome differentiation with good feasibility, but the sensitivity was low. Therefore, this study will use hierarchical Bayesian statistical method to improve the sensitivity and applicability of N-of-1 trials of TCM. Methods/Design. This is a randomized, double-blind, placebo-controlled, three-pair crossover trial for a single subject, including 4-8 weeks of run-in period and 24 weeks of formal trial. In this study, we will recruit a total of 30 participants who are in the stable stage of bronchiectasis. The trial will be divided into three pairs (cycles), and one cycle contains two observation periods. The medications will be taken for three weeks and stopped for one week in the last week of each observation period. The order of syndrome differentiation decoction and placebo will be randomly determined. Patient self-reported symptom score (on a 7-point Likert scale) is the primary outcome. Discussion. Some confounding variables (such as TCM syndrome type and potential carryover effect of TCM) will be introduced into hierarchical Bayesian statistical method to improve the sensitivity and applicability of N-of-1 trials of TCM, and the use of prior available information (e.g., "borrowing from strength" of previous trial results) 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. It is the exploration of improving the objective evaluation method of the clinical efficacy of TCM and may provide reference value for clinical trials of TCM in other chronic diseases. This trial is registered with ClinicalTrials.gov (ID: NCT04601792).

A Bayesian-bandit adaptive design for N-of-1 clinical trials

N-of-1 trials, which are randomized, double-blinded, controlled, multiperiod, crossover trials on a single subject, have been applied to determine the heterogeneity of the individual's treatment effect in precision medicine settings. An aggregated N-of-1 design, which can estimate the population effect from these individual trials, is a pragmatic alternative when a randomized controlled trial (RCT) is infeasible. We propose a Bayesian adaptive design for both the individual and aggregated N-of-1 trials using a multiarmed bandit framework that is estimated via efficient Markov chain Monte Carlo. A Bayesian hierarchical structure is used to jointly model the individual and population treatment effects. Our proposed adaptive trial design is based on Thompson sampling, which randomly allocates individuals to treatments based on the Bayesian posterior probability of each treatment being optimal. While we use a subject-specific treatment effect and Bayesian posterior probability estimates to determine an individual's treatment allocation, our hierarchical model facilitates these individual estimates to borrow strength from the population estimates via shrinkage to the population mean. We present the design's operating characteristics and performance via a simulation study motivated by a recently completed N-of-1 clinical trial. We demonstrate that from a patient-centered perspective, subjects are likely to benefit from our adaptive design, in particular, for those individuals that deviate from the overall population effect.

Developments in evidence creation for treatments of inborn errors of metabolism

Inborn errors of metabolism (IEM) represent the first group of genetic disorders, amenable to causal therapies. In addition to traditional medical diet and cofactor treatments, new treatment strategies such as enzyme replacement and small molecule therapies, solid organ transplantation, and cell-and gene-based therapies have become available. Inherent to the rare nature of the single conditions, generating high-quality evidence for these treatments in clinical trials and under real-world conditions has been challenging. Guidelines developed with standardized methodologies have contributed to improve the practice of care and long-term clinical outcomes. Adaptive trial designs allow for changes in sample size, group allocation and trial duration as the trial proceeds. n-of-1 studies may be used in small sample sized when participants are clinically heterogeneous. Multicenter observational and registry-based clinical trials are promoted via international research networks. Core outcome and standard data element sets will enhance comparative analysis of clinical trials and observational studies. Patient-centered outcome-research as well as patient-led research initiatives will further accelerate the development of therapies for IEM.

Individual responses to topical ibuprofen gel or capsaicin cream for painful knee osteoarthritis: a series of n-of-1 trials

Objectives

To determine individual responses to ibuprofen gel or capsaicin cream for painful, radiographic knee OA using a series of n-of-1 trials.

Methods

Twenty-two participants were allocated 5% ibuprofen gel (A) and 0.025% capsaicin cream (B) in random sequence (AB or BA). Patients underwent up to 3 treatment cycles, each comprising one treatment for 4 weeks, an individualized washout period (maximum 4 weeks), then the other treatment for 4 weeks. Differential (ibuprofen or capsaicin) response was defined when change-from-baseline pain intensity scores (0–10 NRS) differed by ≥1 between treatments in ≥2 cycles within a participant.

Results

A total of 104 treatment periods were aggregated. Mean pain reduction was 1.2 (95% CI: 0.5, 1.8) on ibuprofen and 1.6 (95% CI: 0.9, 2.4) on capsaicin (P = 0.221). Of 22 participants, 4 (18%) had a greater response to ibuprofen, 9 (41%) to capsaicin, 4 (18%) had similar responses, and 5 (23%) were undetermined.

Conclusion

Irrespective of equal efficacy overall, 59% of people displayed a greater response to one treatment over the other. Patients who do not benefit from one type of topical treatment should be offered to try another, which may be more effective. N-of-1 trials are useful to identify individual response to treatment.

Clinical trial registration

https://clinicaltrials.gov, NCT03146689

So near yet so far: why won't the UK prescribe medical cannabis?

Although cannabis-based products for medicinal use are now legal in the UK, it is still challenging for patients to gain access, and only very few National Health Service prescriptions have been written to date. This paper attempts to make sense of why the UK lags behind so many other countries which also have legalised medical cannabis. From consulting with parents and patients, prescribers, pharmacists and decision-makers it seems that there are a series of distinct barriers to prescribing that need to be overcome in order to improve patient access to medical cannabis in the UK. These include concerns about the perceived lack of scientific evidence. To alleviate these concerns, we highlight the importance of patient-centred approaches including patient-reported outcomes, pharmacoepidemiology and n=1 trials, which can contribute to the development of the evidence base for medical cannabis. We hope that this paper will help policymakers and prescribers understand the challenges to prescribing and so help them develop approaches to overcome the current situation which is detrimental to patients.

Treatment Burst Data Points and Single Case Design Studies: A Bayesian N-of-1 Analysis for Estimating Treatment Effect Size

Single-case experimental designs (SCED) evaluate treatment effects for each participant, but it is difficult to aggregate and quantify treatment effects across SCED participants receiving the same type of treatment. We applied Bayesian analytic procedures to SCED data aggregated across participants that have previously only been applied to large-N and group design studies of treatment effect sizes. For the current study, we defined transient elevated treatment data points as (1) above the range of the last five baseline sessions during the first three sessions of treatment (i.e., extinction burst); (2) within or above the range of baseline after the first three treatment sessions (i.e., recurrence burst); or (3) thinning phase data points above the last three prethinning treatment data points (i.e., thinning burst). Results indicated that the treatment effect sizes remained large regardless of controlling for transient elevated treatment data points. Finally, we examined the effects of reinforcer schedule thinning on estimates of treatment effect size. Results indicated a moderate negative impact of schedule thinning on treatment effect size with a 16% decrease in effect size. Recommendations for research and practice are provided, and the utility of using Bayesian analysis in single-case experimental designs is discussed.

Modified Sijunzi decoction in the treatment of ulcerative colitis in the remission phase: study protocol for a series of N-of-1 double-blind, randomised controlled trials

Background

Modified Sijunzi decoction (SJZD) has been used to treat ulcerative colitis (UC) in remission. However, more rigorous clinical trials are necessary to evaluate its effectiveness. Therefore, a series of single-case randomised controlled trials (N-of-1 trials) is proposed to compare the efficacy of modified SJZD with mesalazine for treating UC in remission.

Methods

This is a single-site, hospital-based, double-blind N-of-1 trial for 10 single subjects. Three cycles of N-of-1 trials are planned. There are two treatment periods in each cycle. Modified SJZD combined with mesalazine placebo or mesalazine combined with modified SJZD placebo will be randomised during each 8-week treatment period. There is no washout period in the study. Subjects will be selected by the researcher strictly in accordance with the inclusion and exclusion criteria.

Discussion

Paired t tests and mixed-effect models will be used to analyse the visual analogue scale (VAS) for clinical symptoms and the quality of life questionnaire responses. The findings will be interpreted with caution. We anticipate that the results will show that modified SJZD is effective for patients with UC in remission.

Trial registration

Chinese Clinical Trial Register, ID: ChiCTR1900024086. Registered on 24 June 2019.

The possibilities of the use of N-of-1 and do-it-yourself trials in nutritional research

Background

N-of-1 designs gain popularity in nutritional research because of the improving technological possibilities, practical applicability and promise of increased accuracy and sensitivity, especially in the field of personalized nutrition. This move asks for a search of applicable statistical methods.

Objective

To demonstrate the differences of three popular statistical methods in analyzing treatment effects of data obtained in N-of-1 designs.

Method

We compare Individual-participant data meta-analysis, frequentist and Bayesian linear mixed effect models using a simulation experiment. Furthermore, we demonstrate the merits of the Bayesian model including prior information by analyzing data of an empirical study on weight loss.

Results

The linear mixed effect models are to be preferred over the meta-analysis method, since the individual effects are estimated more accurately as evidenced by the lower errors, especially with lower sample sizes. Differences between Bayesian and frequentist mixed models were found to be small, indicating that they will lead to the same results without including an informative prior.

Conclusion

For empirical data, the Bayesian mixed model allows the inclusion of prior knowledge and gives potential for population based and personalized inference.

Real-Time Monitoring: A Key Element in Personalized Health and Precision Health

Current management of psychiatric disorders relies heavily on retrospective, subjective reports provided by patients and their families. Consequently, psychiatric services are often provisioned inefficiently and with suboptimal outcomes. Recent advances in computing and sensor technologies have enabled the development of real-time monitoring systems for the diagnosis and management of psychiatric disorders. The state of these technologies is rapidly evolving, with passive monitoring and predictive modeling as two areas that have great potential to affect psychiatric care. Although outpatient psychiatry probably stands to benefit the most from the use of real-time monitoring technologies, there are also several ways in which inpatient psychiatry may also benefit. As the capabilities of these technologies increase and their use becomes more common, many ethical and legal issues will need to be considered. The role of governmental regulatory bodies and nongovernmental organizations in providing oversight of the implementation of these technologies is an active area of discussion.

Scientific challenges for precision public health

The notion of ‘precision’ public health has been the subject of much debate, with recent articles coming to its defence following the publication of several papers questioning its value.

Critics of precision public health raise the following problems and questionable assumptions: the inherent limits of prediction for individuals; the limits of approaches to prevention that rely on individual agency, in particular the potential for these approaches to widen inequalities; the undue emphasis on the supposed new information contained in individuals’ molecules and their ‘big data’ at the expense of their own preferences for a particular intervention strategy and the diversion of resources and attention from the social determinants of health.

In order to refocus some of these criticisms of precision public health as scientific questions, this article outlines some of the challenges when defining risk for individuals; the limitations of current theory and study design for precision public health; and the potential for unintended harms.

Analysing N-of-1 observational data in health psychology and behavioural medicine: a 10-step SPSS tutorial for beginners

Background: N-of-1 observational studies can be used to describe natural intra-individual changes in health-related behaviours or symptoms over time, to test behavioural theories and to develop highly personalised health interventions. To date, N-of-1 observational methods have been under-used in health psychology and behavioural medicine. One reason for this may be the perceived complexity of statistical analysis of N-of-1 data.

Objective: This tutorial paper describes a 10-step procedure for the analysis of N-of-1 observational data using dynamic regression modelling in SPSS for researchers, students and clinicians who are new to this area. The 10-step procedure is illustrated using real data from an N-of-1 observational study exploring the relationship between pain and physical activity.

Conclusion: The availability of a user-friendly and robust statistical technique for the analysis of N-of-1 data using SPSS may foster increased awareness, knowledge and skills and establish N-of-1 designs as a useful methodological tool in health psychology and behavioural medicine.

Practice-Based Research in Complementary Medicine: Could N-of-1 Trials Become the New Gold Standard?

Complementary medicines and therapies are popular forms of healthcare with a long history of traditional use. Yet, despite increasing consumer demand, there is an ongoing exclusion of complementary medicines from mainstream healthcare systems. A lack of evidence is often cited as justification. Until recently, high-quality evidence of treatment efficacy was defined as findings from well-conducted systematic reviews and meta-analyses of randomized controlled trials. In a recent and welcome move by the Oxford Centre for Evidence-Based Practice, however, the N-of-1 trial design has also been elevated to the highest level of evidence for treatment efficacy of an individual, placing this research design on par with the meta-analysis. N-of-1 trial designs are experimental research methods that can be implemented in clinical practice. They incorporate much of the rigor of group clinical trials, but are designed for individual patients. Individualizing treatment interventions and outcomes in research designs is consistent with the movement towards patient-centered care and aligns well with the principles of holism as practiced by naturopaths and many other complementary medicine practitioners. This paper explores whether rigorously designed and conducted N-of-1 trials could become a new ‘gold standard’ for demonstrating treatment efficacy for complementary medicine interventions in individual patients in clinical practice.

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach-phenotypic personalized medicine (PPM)-finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals' lives.

Comparison of Aggregated N-of-1 Trials with Parallel and Crossover Randomized Controlled Trials Using Simulation Studies

Background: N-of-1 trials offer an innovative approach to delivering personalized clinical care together with population-level research. While increasingly used, these methods have raised some statistical concerns in the healthcare community. Methods: We discuss concerns of selection bias, carryover effects from treatment, and trial data analysis conceptually, then rigorously evaluate concerns of effect sizes, power and sample size through simulation study. Four variance structures for patient heterogeneity and model error are considered in a series of 5000 simulated trials with 3 cycles, which compare aggregated N-of-1 trials to parallel randomized controlled trials (RCTs) and crossover trials. Results: Aggregated N-of-1 trials outperformed both traditional parallel RCT and crossover designs when these trial designs were simulated in terms of power and required sample size to obtain a given power. N-of-1 designs resulted in a higher type-I error probability than parallel RCT and cross over designs when moderate-to-strong carryover effects were not considered or in the presence of modeled selection bias. However, N-of-1 designs allowed better estimation of patient-level random effects. These results reinforce the need to account for these factors when planning N-of-1 trials. Conclusion: N-of-1 trial designs offer a rigorous method for advancing personalized medicine and healthcare with the potential to minimize costs and resources. Interventions can be tested with adequate power with far fewer patients than traditional RCT and crossover designs. Operating characteristics compare favorably to both traditional RCT and crossover designs.

RD-RAP: beyond rare disease patient registries, devising a comprehensive data and analytic framework

Within the 21 APEC economies alone, there are an estimated 200 million individuals living with a rare disease. As such, health data on these individuals, and hence patient registries, are vital. However, registries can come in many different forms and operating models in different jurisdictions. They possess a varying degree of functionality and are used for a variety of purposes. For instance registries can facilitate service planning as well as underpin public health and clinical research by providing de-identified data to researchers. Furthermore, registries may be used to create and disseminate new knowledge to inform clinical best practice and care, to identify and enrol participants for clinical trials, and to enable seamless integration of patient data for diagnostic testing and cascade screening. Registries that add capability such as capturing patient reported outcomes enable patients, and their carers, to become active partners in their care, rapidly furthering research and ensuring up-to-date practice-based evidence. Typically, a patient registry centres around the notion of health data 'capture', usually for only one or a small subset of the functions outlined above, thereby creating fragmented datasets that, despite the best efforts and intentions, make it difficult to exchange the right data for the right purpose to the right stakeholder under appropriate governance arrangements. Trying to incorporate maximum functionality into a registry is an obvious strategy, but monolithic software solutions are not desirable. As an alternative, we propose that it is important to incorporate analytics as core to a patient registry, rather than just utilising registries as a 'data capture' solution. We contend that embracing an analytics-centric focus makes it reasonable to imagine a future where it will be possible to evaluate the individual outcomes of health interventions in real time. The purposeful and, importantly, the repurposable application of health data will allow stakeholders to extract, create and reuse knowledge to improve health outcomes, assist clinical decision making, and improve health service design and delivery. To realise this vision, we introduce and describe the concept of a Rare Disease Registry and Analytics Platform (RD-RAP); one that we hope will make a meaningful difference to the lives of those living with a rare disease.

Power and Design Issues in Crossover-Based N-Of-1 Clinical Trials with Fixed Data Collection Periods

“N-of-1,” or single subject, clinical trials seek to determine if an intervention strategy is more efficacious for an individual than an alternative based on an objective, empirical, and controlled study. The design of such trials is typically rooted in a simple crossover strategy with multiple intervention response evaluation periods. The effect of serial correlation between measurements, the number of evaluation periods, the use of washout periods, heteroscedasticity (i.e., unequal variances among responses to the interventions) and intervention-associated carry-over phenomena on the power of such studies is crucially important for putting the yield and feasibility of N-of-1 trial designs into context. We evaluated the effect of these phenomena on the power of different designs for N-of-1 trials using analytical theory based on standard likelihood principles assuming an autoregressive lag 1, i.e., AR(1), serial correlation structure among the measurements as well as simulation studies. By evaluating the power to detect effects in many different settings, we show that the influence of serial correlation and heteroscedasticity on power can be substantial, but can also be mitigated to some degree through the use of appropriate multiple evaluation periods. We also show that the detection of certain types of carry-over effects can be heavily influenced by design considerations as well.