Understanding Variation in Sets of N-of-1 Trials

Behavioral carry-over effect and power consideration in crossover trials

A crossover trial is an efficient trial design when there is no carry-over effect. To reduce the impact of the biological carry-over effect, a washout period is often designed. However, the carry-over effect remains an outstanding concern when a washout period is unethical or cannot sufficiently diminish the impact of the carry-over effect. The latter can occur in comparative effectiveness research, where the carry-over effect is often non-biological but behavioral. In this paper, we investigate the crossover design under a potential outcomes framework with and without the carry-over effect. We find that when the carry-over effect exists and satisfies a sign condition, the basic estimator underestimates the treatment effect, which does not inflate the type I error of one-sided tests but negatively impacts the power. This leads to a power trade-off between the crossover design and the parallel-group design, and we derive the condition under which the crossover design does not lead to type I error inflation and is still more powerful than the parallel-group design. We also develop covariate adjustment methods for crossover trials. We evaluate the performance of cross-over design and covariate adjustment using data from the MTN-034/REACH study.

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.

Cannabidiol (Epidyolex®) for severe behavioral manifestations in patients with tuberous sclerosis complex, mucopolysaccharidosis type III and fragile X syndrome: protocol for a series of randomized, placebo-controlled N-of-1 trials

BACKGROUND

Many rare genetic neurodevelopmental disorders (RGNDs) are characterized by intellectual disability (ID), severe cognitive and behavioral impairments, potentially diagnosed as a comorbid autism spectrum disorder or attention-deficit hyperactivity disorder. Quality of life is often impaired due to irritability, aggression and self-injurious behavior, generally refractory to standard therapies. There are indications from previous (case) studies and patient reporting that cannabidiol (CBD) may be an effective treatment for severe behavioral manifestations in RGNDs. However, clear evidence is lacking and interventional research is challenging due to the rarity as well as the heterogeneity within and between disease groups and interindividual differences in treatment response. Our objective is to examine the effectiveness of CBD on severe behavioral manifestations in three RGNDs, including Tuberous Sclerosis Complex (TSC), mucopolysaccharidosis type III (MPS III), and Fragile X syndrome (FXS), using an innovative trial design.

METHODS

We aim to conduct placebo-controlled, double-blind, block-randomized, multiple crossover N-of-1 studies with oral CBD (twice daily) in 30 patients (aged ≥ 6 years) with confirmed TSC, MPS III or FXS and severe behavioral manifestations. The treatment is oral CBD up to a maximum of 25 mg/kg/day, twice daily. The primary outcome measure is the subscale irritability of the Aberrant Behavior Checklist. Secondary outcome measures include (personalized) patient-reported outcome measures with regard to behavioral and psychiatric outcomes, disease-specific outcome measures, parental stress, seizure frequency, and adverse effects of CBD. Questionnaires will be completed and study medication will be taken at the participants' natural setting. Individual treatment effects will be determined based on summary statistics. A mixed model analysis will be applied for analyzing the effectiveness of the intervention per disorder and across disorders combining data from the individual N-of-1 trials.

DISCUSSION

These N-of-1 trials address an unmet medical need and will provide information on the effectiveness of CBD for severe behavioral manifestations in RGNDs, potentially generating generalizable knowledge at an individual-, disorder- and RGND population level.

TRIAL REGISTRATION

EudraCT: 2021-003250-23, registered 25 August 2022, https://www.clinicaltrialsregister.eu/ctr-search/trial/2021-003250-23/NL .

Are There Interindividual Responses of Cardiovascular Disease Risk Markers to Acute Exercise? A Replicate Crossover Trial

PURPOSE

Using a replicated crossover design, we quantified the response heterogeneity of postprandial cardiovascular disease risk marker responses to acute exercise.

METHODS

Twenty men (mean (SD) age, 26 (6) yr; body mass index, 23.9 (2.4) kg·m -2 ) completed four 2-d conditions (two control, two exercise) in randomized orders. On days 1 and 2, participants rested and consumed two high-fat meals over 9 h. Participants ran for 60 min (61 (7)% of peak oxygen uptake) on day 1 (6.5 to 7.5 h) of both exercise conditions. Time-averaged total area under the curve (TAUC) for triacylglycerol, glucose, and insulin were calculated from 11 venous blood samples on day 2. Arterial stiffness and blood pressure responses were calculated from measurements at baseline on day 1 and at 2.5 h on day 2. Consistency of individual differences was explored by correlating the two replicates of control-adjusted exercise responses for each outcome. Within-participant covariate-adjusted linear mixed models quantified participant-by-condition interactions and individual response SDs.

RESULTS

Acute exercise reduced mean TAUC-triacylglycerol (-0.27 mmol·L -1 ·h; Cohen's d = 0.29, P = 0.017) and TAUC-insulin (-25 pmol·L -1 ·h; Cohen's d = 0.35, P = 0.022) versus control, but led to negligible changes in TAUC-glucose and the vascular outcomes (Cohen's d ≤ 0.36, P ≥ 0.106). Small-to-moderate, but nonsignificant, correlations were observed between the two response replicates ( r = -0.42 to 0.15, P ≥ 0.066). We did not detect any individual response heterogeneity. All participant-by-condition interactions were P ≥ 0.137, and all individual response SDs were small with wide 95% confidence intervals overlapping zero.

CONCLUSIONS

Large trial-to-trial within-subject variability inhibited detection of consistent interindividual variability in postprandial metabolic and vascular responses to acute exercise.

Design, analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research

It is increasingly assumed that there is no one-size-fits-all approach to dietary recommendations for the management and treatment of chronic diseases such as obesity. This phenomenon that not all individuals respond uniformly to a given treatment has become an area of research interest given the rise of personalized and precision medicine. To conduct, interpret, and disseminate this research rigorously and with scientific accuracy, however, requires an understanding of treatment response heterogeneity. Here, we define treatment response heterogeneity as it relates to clinical trials, provide statistical guidance for measuring treatment response heterogeneity, and highlight study designs that can quantify treatment response heterogeneity in nutrition and obesity research. Our goal is to educate nutrition and obesity researchers in how to correctly identify and consider treatment response heterogeneity when analyzing data and interpreting results, leading to rigorous and accurate advancements in the field of personalized medicine.

Biofeedback and Digitalized Motivational Interviewing to Increase Daily Physical Activity: Series of Factorial N-of-1 Randomized Controlled Trials Piloting the Precious App

BACKGROUND

Insufficient physical activity is a public health concern. New technologies may improve physical activity levels and enable the identification of its predictors with high accuracy. The Precious smartphone app was developed to investigate the effect of specific modular intervention elements on physical activity and examine theory-based predictors within individuals.

OBJECTIVE

This study pilot-tested a fully automated factorial N-of-1 randomized controlled trial (RCT) with the Precious app and examined whether digitalized motivational interviewing (dMI) and heart rate variability-based biofeedback features increased objectively recorded steps. The secondary aim was to assess whether daily self-efficacy and motivation predicted within-person variability in daily steps.

METHODS

In total, 15 adults recruited from newspaper advertisements participated in a 40-day factorial N-of-1 RCT. They installed 2 study apps on their phones: one to receive intervention elements and one to collect ecological momentary assessment (EMA) data on self-efficacy, motivation, perceived barriers, pain, and illness. Steps were tracked using Xiaomi Mi Band activity bracelets. The factorial design included seven 2-day biofeedback interventions with a Firstbeat Bodyguard 2 (Firstbeat Technologies Ltd) heart rate variability sensor, seven 2-day dMI interventions, a wash-out day after each intervention, and 11 control days. EMA questions were sent twice per day. The effects of self-efficacy, motivation, and the interventions on subsequent steps were analyzed using within-person dynamic regression models and aggregated data using longitudinal multilevel modeling (level 1: daily observations; level 2: participants). The analyses were adjusted for covariates (ie, within- and between-person perceived barriers, pain or illness, time trends, and recurring events).

RESULTS

All participants completed the study, and adherence to activity bracelets and EMA measurements was high. The implementation of the factorial design was successful, with the dMI features used, on average, 5.1 (SD 1.0) times of the 7 available interventions. Biofeedback interventions were used, on average, 5.7 (SD 1.4) times out of 7, although 3 participants used this feature a day later than suggested and 1 did not use it at all. Neither within- nor between-person analyses revealed significant intervention effects on step counts. Self-efficacy predicted steps in 27% (4/15) of the participants. Motivation predicted steps in 20% (3/15) of the participants. Aggregated data showed significant group-level effects of day-level self-efficacy (B=0.462; P<.001), motivation (B=0.390; P<.001), and pain or illness (B=-1524; P<.001) on daily steps.

CONCLUSIONS

The automated factorial N-of-1 trial with the Precious app was mostly feasible and acceptable, especially the automated delivery of the dMI components, whereas self-conducted biofeedback measurements were more difficult to time correctly. The findings suggest that changes in self-efficacy and motivation may have same-day effects on physical activity, but the effects vary across individuals. This study provides recommendations based on the lessons learned on the implementation of factorial N-of-1 RCTs.

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.

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.

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 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.

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.

Heterogeneity in response to repeated intranasal oxytocin in schizophrenia and autism spectrum disorders: A meta-analysis of variance

Intranasal oxytocin (OT) has been suggested as a putative adjunctive treatment for patients with schizophrenia and autism spectrum disorders (ASD). Here, we examine available evidence from trials investigating the effects of repeated administrations of intranasal OT on the core symptoms of patients with schizophrenia and ASD, focusing on its therapeutic efficacy and heterogeneity of response (meta-ANOVA). Repeated administration of intranasal OT does not improve most of the core symptoms of schizophrenia and ASD, beyond a small tentative effect on schizophrenia general symptoms. However, we found significant moderator effects for dose in schizophrenia total psychopathology and positive symptoms, and percentage of included men and duration of treatment in schizophrenia general symptoms. We found evidence of heterogeneity (increased variance) in the response of schizophrenia negative symptoms to intranasal OT compared with placebo, suggesting that subgroups of responsive and non-responsive patients might coexist. For other core symptoms of schizophrenia, or any of the core symptom dimensions in ASD, the response to repeated treatment with intranasal OT did not show evidence of heterogeneity.

Systematic Review of N-of-1 Studies in Rare Genetic Neurodevelopmental Disorders: The Power of 1

OBJECTIVE

To improve the use of N-of-1 studies in rare genetic neurodevelopmental disorders, we systematically reviewed the literature and formulated recommendations for future studies.

METHODS

The systematic review protocol was registered in the PROSPERO International Prospective Register of Systematic Reviews (CRD42020154720). EMBASE and MEDLINE were searched for relevant studies. Information was recorded on types of interventions, outcome measures, validity, strengths, and limitations using standard reporting guidelines and critical appraisal tools. Qualitative and descriptive analyses were performed.

RESULTS

Twelve studies met the N-of-1 inclusion criteria, including both single trials and series. Interventions were mainly directed to neuropsychiatric manifestations. Main strengths were the use of personalized and clinically relevant outcomes in most studies. Generalizability was compromised due to limited use of validated and generalizable outcome measures.

CONCLUSION

N-of-1 studies are sporadically reported in rare genetic neurodevelopmental disorders. Properly executed N-of-1 studies may provide a powerful alternative to larger randomized controlled trials in rare disorders and a much needed bridge between practice and science. We provide recommendations for future N-of-1 studies in rare genetic neurodevelopmental disorders, ultimately optimizing evidence-based and personalized care.

Perspective: Application of N-of-1 Methods in Personalized Nutrition Research

Personalized and precision nutrition aim to examine and improve health on an individual level, and this requires reconsideration of traditional dietary interventions or behavioral study designs. The limited frequency of measurements in group-level human nutrition trials cannot be used to infer individual responses to interventions, while in behavioral studies, retrospective data collection does not provide an accurate measure of how everyday behaviors affect individual health. This review introduces the concept of N-of-1 study designs, which involve the repeated measurement of a health outcome or behavior on an individual level. Observational designs can be used to monitor a participant's usual health or behavior in a naturalistic setting, with repeated measurements conducted in real time using an Ecological Momentary Assessment. Interventional designs can introduce a dietary or behavioral intervention with predictors and outcomes of interest measured repeatedly either during or after 1 or more intervention and control periods. Due to their flexibility, N-of-1 designs can be applied to both short-term physiological studies and longer-term studies of eating behaviors. As a growing number of disease markers can be measured outside of the clinic, with self-reported data delivered via electronic devices, it is now easier than ever to generate large amounts of data on an individual level. Statistical techniques can be utilized to analyze changes in an individual or to aggregate data from sets of N-of-1 trials, enabling hypotheses to be tested on a small number of heterogeneous individuals. Although their designs necessitate extra methodological and statistical considerations, N-of-1 studies could be used to investigate complex research questions and to study underrepresented groups. This may help to reveal novel associations between participant characteristics and health outcomes, with repeated measures providing power and precision to accurately determine an individual's health status.

Finite Element Analysis of Generalized Ligament Laxity on the Deterioration of Hallux Valgus Deformity (Bunion)

Hallux valgus is a common foot problem affecting nearly one in every four adults. Generalized ligament laxity was proposed as the intrinsic cause or risk factor toward the development of the deformity which was difficult to be investigated by cohort clinical trials. Herein, we aimed to evaluate the isolated influence of generalized ligament laxity on the deterioration using computer simulation (finite element analysis). We reconstructed a computational foot model from a mild hallux valgus participant and conducted a gait analysis to drive the simulation of walking. Through parametric analysis, the stiffness of the ligaments was impoverished at different degrees to resemble different levels of generalized ligament laxity. Our simulation study reported that generalized ligament laxity deteriorated hallux valgus by impairing the load-bearing capacity of the first metatarsal, inducing higher deforming force, moment and malalignment at the first metatarsophalangeal joint. Besides, the deforming moment formed a deteriorating vicious cycle between hallux valgus and forefoot abduction and may result in secondary foot problems, such as flatfoot. However, the metatarsocuneiform joint did not show a worsening trend possibly due to the overriding forefoot abduction. Controlling the deforming load shall be prioritized over the correction of angles to mitigate deterioration or recurrence after surgery.

Optimizing Aggregated N-Of-1 Trial Designs for Predictive Biomarker Validation: Statistical Methods and Theoretical Findings

Background and Significance: Parallel-group randomized controlled trials (PG-RCTs) are the gold standard for detecting differences in mean improvement across treatment conditions. However, PG-RCTs provide limited information about individuals, making them poorly optimized for quantifying the relationship of a biomarker measured at baseline with treatment response. In N-of-1 trials, an individual subject moves between treatment conditions to determine their specific response to each treatment. Aggregated N-of-1 trials analyze a cohort of such participants, and can be designed to optimize both statistical power and clinical or logistical constraints, such as allowing all participants to begin with an open-label stabilization phase to facilitate the enrollment of more acutely symptomatic participants. Here, we describe a set of statistical simulation studies comparing the power of four different trial designs to detect a relationship between a predictive biomarker measured at baseline and subjects' specific response to the PTSD pharmacotherapeutic agent prazosin.

Methods: Data was simulated from 4 trial designs: (1) open-label; (2) open-label + blinded discontinuation; (3) traditional crossover; and (4) open label + blinded discontinuation + brief crossover (the N-of-1 design). Designs were matched in length and assessments. The primary outcome, analyzed with a linear mixed effects model, was whether a statistically significant association between biomarker value and response to prazosin was detected with 5% Type I error. Simulations were repeated 1,000 times to determine power and bias, with varied parameters.

Results: Trial designs 2 & 4 had substantially higher power with fewer subjects than open label design. Trial design 4 also had higher power than trial design 2. Trial design 4 had slightly lower power than the traditional crossover design, although power declined much more rapidly as carryover was introduced.

Conclusions: These results suggest that an aggregated N-of-1 trial design beginning with an open label titration phase may provide superior power over open label or open label and blinded discontinuation designs, and similar power to a traditional crossover design, in detecting an association between a predictive biomarker and the clinical response to the PTSD pharmacotherapeutic prazosin. This is achieved while allowing all participants to spend the first 8 weeks of the trial on open-label active treatment.

A Review of Challenges and Opportunities in Machine Learning for Health

Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example, diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented. This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare.

"Asking Too Much?": Randomized N-of-1 Trial Exploring Patient Preferences and Measurement Reactivity to Frequent Use of Remote Multidimensional Pain Assessments in Children and Young People With Juvenile Idiopathic Arthritis

Background

Remote monitoring of pain using multidimensional mobile health (mHealth) assessment tools is increasingly being adopted in research and care. This assessment method is valuable because it is challenging to capture pain histories, particularly in children and young people in diseases where pain patterns can be complex, such as juvenile idiopathic arthritis (JIA). With the growth of mHealth measures and more frequent assessment, it is important to explore patient preferences for the timing and frequency of administration of such tools and consider whether certain administrative patterns can directly impact on children’s pain experiences.

Objective

This study aimed to explore the feasibility and influence (in terms of objective and subjective measurement reactivity) of several time sampling strategies in remote multidimensional pain reporting.

Methods

An N-of-1 trial was conducted in a subset of children and young people with JIA and their parents recruited to a UK cohort study. Children were allocated to 1 of 4 groups. Each group followed a different schedule of completion of MPT for 8 consecutive weeks. Each schedule included 2 blocks, each comprising 4 different randomized time sampling strategies, with each strategy occurring once within each 4-week block. Children completed MPT according to time sampling strategies: once-a-day, twice-a-day, once-a-week, and as-and-when pain was experienced. Adherence to each strategy was calculated. Participants completed the Patient-Reported Outcomes Measurement Information System Pain Interference Scale at the end of each week to explore objective reactivity. Differences in pain interference scores between time sampling strategies were assessed graphically and using Friedman tests. Children and young people and their parents took part in a semistructured interview about their preferences for different time sampling strategies and to explore subjective reactivity.

Results

A total of 14 children and young people (aged 7-16 years) and their parents participated. Adherence to pain reporting was higher in less intense time sampling strategies (once-a-week=63% [15/24]) compared with more intense time sampling strategies (twice-a-day=37.8% [127/336]). There were no statistically significant differences in pain interference scores between sampling strategies. Qualitative findings from interviews suggested that children preferred once-a-day (6/14, 43%) and as-and-when pain reporting (6/14, 43%). Creating routine was one of the most important factors for successful reporting, while still ensuring that comprehensive information about recent pain was captured.

Conclusions

Once-a-day pain reporting provides rich contextual information. Although patients were less adherent to this preferred sampling strategy, once-a-day reporting still provides more frequent assessment opportunities compared with other less intense or overburdensome schedules. Important issues for the design of studies and care incorporating momentary assessment techniques were identified. We demonstrate that patient reporting preferences are key to accommodate and are important where data capture quality is key. Our findings support frequent administration of such tools, using daily reporting methods where possible.

A Review of Rare Disease Policies and Orphan Drug Reimbursement Systems in 12 Eurasian Countries

Background: Despite international initiatives on collaboration within the field of rare diseases, patient access to orphan medicinal products (OMPs) and healthcare services differ greatly between countries. This study aimed to create a comprehensive and in-depth overview of rare diseases policies and reimbursement of OMPs in a selection of 12 countries in the Western Eurasian region: Armenia, France, Germany, Kazakhstan, Latvia, The Netherlands, Poland, Romania, Russia, Turkey, Ukraine, and the United Kingdom. Methods: A systematic literature review was performed and an analysis of publicly available legislative and rare disease health policy data was undertaken in five focus areas: rare disease definition, newborn screening, registries, national plans, access to/reimbursement of OMPs. Results: Screening programs are broadly implemented but the number of screened diseases differs significantly (2-35 diseases), either between EU and non-EU countries, between EU member states and sometimes even within a single country. In most countries rare disease registries are operating with regional, national, European or worldwide coverage. The number of rare disease registries is growing, as a result of the National Plans (EU) and increased international scientific cooperation. France, Russia, and Poland have a centrally acting registry. National plans are present in all EU countries but implementation varies and is ongoing. The number of reimbursed OMPs in the selected countries ranges from nearly all available OMPs in the Netherlands, Germany, and France to zero in Armenia. Reimbursement rules differ considerably regionally and a trend is observed of reimbursement conditions getting stricter for expensive (orphan) drugs. Discussion: Inequality in patient access to new OMPs still exists due to variations in national policies, healthcare budgets, health insurance, and reimbursement systems. The observed differences are challenging for rare disease patients, health authorities and manufacturers alike. Progress can be seen, however, and international cooperation and harmonization is slowly but steadily expanding in the rare disease arena.

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.

What research for what training in psychotherapy? Some methodological issues and a proposal

To define boundaries and links between research and training in psychotherapy we have to establish what kind of research is needed for this purpose. For defining psychotherapy as a science some basic epistemological premises should be affirmed and specific methods have to be devised, using both quantitative and qualitative approaches, diachronic and longitudinal perspectives, cumulative and meta-analytic strategies, focusing both the techniques used in the therapies and the relationship between the therapist subject and the client subject as a core mean for produce change. What should be evaluated in this research process, what methods and techniques of assessment should be preferred, what analyses of data are suitable: these are the main issues addressed in the article, as they are useful for planning the training of a therapist as a researcher, regardless of the privileged theoretical and technical approach. Science and practice have to be connected, since they both allow the monitoring of what occurs within the confines of the therapy and favor exchange among psychotherapists from differing theoretical approaches, which also increases their external visibility in the scientific community and in a wider social context. The goal of fostering scientific attitudes in the psychotherapists needs a specific training, to acquire a research mindedness also out of the academic laboratories. A cooperation among scientific associations and institutions is proposed to reach these objectives necessary for psychotherapists' trainings including competencies in evaluating and sharing the scientific aspects of their work.

Don’t abandon RCTs in IVF. We don’t even understand them

The conclusion of the Human Fertilisation and Embryology Authority that ‘add-on’ therapies in IVF are not supported by high-quality evidence has prompted new questions regarding the role of the randomized controlled trial (RCT) in evaluating infertility treatments. Critics argue that trials are cumbersome tools that provide irrelevant answers. Instead, they argue that greater emphasis should be placed on large observational databases, which can be analysed using powerful algorithms to determine which treatments work and for whom. Although the validity of these arguments rests upon the sciences of statistics and epidemiology, the discussion to date has largely been conducted without reference to these fields. We aim to remedy this omission, by evaluating the arguments against RCTs in IVF from a primarily methodological perspective. We suggest that, while criticism of the status quo is warranted, a retreat from RCTs is more likely to make things worse for patients and clinicians.

Evaluation of Differences in Individual Treatment Response in Schizophrenia Spectrum Disorders: A Meta-analysis

IMPORTANCE

An assumption among clinicians and researchers is that patients with schizophrenia vary considerably in their response to antipsychotic drugs in randomized clinical trials (RCTs).

OBJECTIVE

To evaluate the overall variation in individual treatment response from random variation by comparing the variability between treatment and control groups.

DATA SOURCES

Cochrane Schizophrenia, MEDLINE/PubMed, Embase, PsycINFO, Cochrane CENTRAL, BIOSIS Previews, ClinicalTrials.gov, and World Health Organization International Clinical Trials Registry Platform from January 1, 1955, to December 31, 2016.

STUDY SELECTION

Double-blind, placebo-controlled, RCTs of adults with a diagnosis of schizophrenia spectrum disorders and prescription for licensed antipsychotic drugs.

DATA EXTRACTION AND SYNTHESIS

Means and SDs of the Positive and Negative Syndrome Scale pretreatment and posttreatment outcome difference scores were extracted. Data quality and validity were ensured by following the PRISMA guidelines.

MAIN OUTCOMES AND MEASURES

The outcome measure was the overall variability ratio of treatment to control in a meta-analysis across RCTs. Individual variability ratios were weighted by the inverse-variance method and entered into a random-effects model. A personal element of response was hypothesized to be reflected by a substantial overall increase in variability in the treatment group compared with the control group.

RESULTS

An RCT was simulated, comprising 30 patients with schizophrenia randomized to either the treatment or the control group. The different components of variation in RCTs were illustrated with simulated data. In addition, we assessed the variability ratio in 52 RCTs involving 15 360 patients with a schizophrenia or schizoaffective diagnosis. The variability was slightly lower in the treatment compared with the control group (variability ratio = 0.97; 95% CI, 0.95-0.99; P = .01).

CONCLUSIONS AND RELEVANCE

In this study, no evidence was found in RCTs that antipsychotic drugs increased the outcome variance, suggesting no personal element of response to treatment but instead indicating that the variance was slightly lower in the treatment group than in the control group; although the study cannot rule out that subsets of patients respond differently to treatment, it suggests that the average treatment effect is a reasonable assumption for the individual patient.

Demonstrating Heterogeneity of Treatment Effects Among Patients: An Overlooked but Important Step Toward Precision Medicine

Although heterogeneity in the observed outcomes in clinical trials is often assumed to reflect a true heterogeneous response, it could actually be due to random variability. This retrospective analysis of four randomized, double-blind, placebo-controlled multiperiod (i.e., episode) crossover trials of fentanyl for breakthrough cancer pain illustrates the use of multiperiod crossover trials to examine heterogeneity of treatment response. A mixed-effects model, including fixed effects for treatment and episode and random effects for patient and treatment-by-patient interaction, was used to assess the heterogeneity in patients' responses to treatment during each episode. A significant treatment-by-patient interaction was found for three of four trials (P < 0.05), suggesting heterogeneity of the effect of fentanyl among different patients in each trial. Similar analyses in other therapeutic areas could identify conditions and therapies that should be investigated further for predictors of treatment response in efforts to maximize the efficiency of developing precision medicine strategies.

Personalized Medicine and the Treatment of Hypertension

PURPOSE OF REVIEW

The purpose of this review is to discuss the implications of personalized medicine for the treatment of hypertension, including resistant hypertension.

RECENT FINDINGS

We suggest a framework for the personalized treatment of hypertension based on the concept of a trade-off between simplicity and personalization. This framework is based on treatment strategies classified as low, medium, or high information burden personalization approaches. The extent to which a higher information burden is justified depends on the clinical scenario, particularly the ease with which the blood pressure can be controlled. A one-size-fits-many treatment strategy for hypertension is efficacious for most people; however, a more personalized approach could be useful in patients with subtypes of hypertension that do not respond as expected to treatment. Clinicians seeing patients with unusual hypertension phenotypes should be familiar with emerging trends in personalized treatment of hypertension.

Random main effects of treatment: A case study with a network meta-analysis

If the number of treatments in a network meta-analysis is large, it may be possible and useful to model the main effect of treatment as random, that is to say as random realizations from a normal distribution of possible treatment effects. This then constitutes a third sort of random effect that may be considered in connection with such analyses. The first and most common models treatment-by-trial interaction as being random and the second, rather rarer, models the main effects of trial as being random and thus permits the recovery of intertrial information. Taking the example of a network meta-analysis of 44 similar treatments in 10 trials, we illustrate how a hierarchical approach to modeling a random main effect of treatment can be used to produce shrunk (toward the overall mean) estimates of effects for individual treatments. As a related problem, we also consider the issue of using a random-effect model for the within-trial variances from trial to trial. We provide a number of possible graphical representations of the results and discuss the advantages and disadvantages of such an approach.

The Precision HYpertenSIon Care (PHYSIC) study: a double-blind, randomized, repeated cross-over study

High blood pressure is the leading risk factor for premature deaths and a major cost to societies worldwide. Effective blood pressure-lowering drugs are available, but patient adherence to them is low, likely partly due to side effects. To identify patient-specific differences in treatment effects, a repeated cross-over design, where the same treatment contrasts are repeated within each patient, is needed. Such designs have been surprisingly rarely used, given the current focus on precision medicine. The Precision HYpertenSIon Care (PHYSIC) study aims to investigate if there is a consistent between-person variation in blood pressure response to the common blood pressure-lowering drug classes of a clinically relevant magnitude, given the within-person variation in blood pressure. The study will also investigate the between-person variation in side effects of the drugs. In a double-blind, randomized, repeated cross-over trial, 300 patients with mild hypertension will be treated with four blood pressure-lowering drugs (candesartan, lisinopril, amlodipine, and hydrochlorothiazide) in monotherapy, with two of the drugs repeated for each patient. If the study indicates that there is a potential for precision hypertension care, the most promising predictors of blood pressure and side effect response to the drugs will be explored, as will the potential for development of a biomarker panel to rank the suitability of blood pressure-lowering drug classes for individual patients in terms of anticipated blood pressure effects and side effects, with the ultimate goal to maximize adherence. The study follows a protocol pre-registered at ClinicalTrials.gov with the identifier NCT02774460.

The Applicability of N: Ancient Debates and Modern Experimental Design

Medicine has always been characterized by a tension between the particular and the general. A clinician is obligated to treat the individual in front of her, yet she accomplishes this task by applying generalized knowledge that describes an abstract average but not necessarily a specific person. Efforts to systematize this process of moving between the particular and the general have led to the development of randomized controlled trials and large observational studies. Inclusion of tens of thousands of people in such studies, it is argued, will enhance the applicability of the data to more individual circumstances. Yet, as genetic sequencing data have become more widely obtained and used, there has been an increased focus on what has been broadly termed "precision medicine", a highly individualized approach to therapeutics. Moreover, advances in statistical methods have enabled researchers to use N-of-1 study data-traditionally considered too individualized to be broadly applicable-in new ways. This paper contextualizes these apparently modern debates with reference to historical arguments about methods of disease diagnosis and treatment, and earlier physicians' concerns about the tension between the particular and the general that is intrinsic to medical practice.

Advancing personalized treatment of Alzheimer's disease: a call for the N-of-1 trial design

There has not been a new treatment for Alzheimer's disease (AD) for over a decade, with a large number of Phase II/III randomized clinical trials failing. Randomized clinical trials examine group effects that may be difficult to extrapolate to the individual patient given the multifactorial pathogenic processes associated with AD, and are increasingly long in duration, expensive to run, requiring large sample sizes that are difficult to recruit. An alternative approach is to consider N-of-1 trial designs. The N-of-1 trial is ideal to evaluate effectiveness of interventions for chronic conditions combining the rigor of a randomized trial with the tailoring of therapy to an individual. This review examines the N-of-1 design, its benefits and limitations, and how it could be implemented to investigate new therapies for AD.

Lessons learned from IDeAl - 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials

BACKGROUND

IDeAl (Integrated designs and analysis of small population clinical trials) is an EU funded project developing new statistical design and analysis methodologies for clinical trials in small population groups. Here we provide an overview of IDeAl findings and give recommendations to applied researchers.

METHOD

The description of the findings is broken down by the nine scientific IDeAl work packages and summarizes results from the project's more than 60 publications to date in peer reviewed journals. In addition, we applied text mining to evaluate the publications and the IDeAl work packages' output in relation to the design and analysis terms derived from in the IRDiRC task force report on small population clinical trials.

RESULTS

The results are summarized, describing the developments from an applied viewpoint. The main result presented here are 33 practical recommendations drawn from the work, giving researchers a comprehensive guidance to the improved methodology. In particular, the findings will help design and analyse efficient clinical trials in rare diseases with limited number of patients available. We developed a network representation relating the hot topics developed by the IRDiRC task force on small population clinical trials to IDeAl's work as well as relating important methodologies by IDeAl's definition necessary to consider in design and analysis of small-population clinical trials. These network representation establish a new perspective on design and analysis of small-population clinical trials.

CONCLUSION

IDeAl has provided a huge number of options to refine the statistical methodology for small-population clinical trials from various perspectives. A total of 33 recommendations developed and related to the work packages help the researcher to design small population clinical trial. The route to improvements is displayed in IDeAl-network representing important statistical methodological skills necessary to design and analysis of small-population clinical trials. The methods are ready for use.

Does evidence support the high expectations placed in precision medicine? A bibliographic review

Background: Precision medicine is the Holy Grail of interventions that are tailored to a patient's individual characteristics. However, conventional clinical trials are designed to find differences in averages, and interpreting these differences depends on untestable assumptions. Although only an ideal, a constant effect of treatment would facilitate individual management. A direct consequence of a constant effect is that the variance of the outcome measure would be the same in the treated and control arms. We reviewed the literature to explore the similarity of these variances as a foundation for examining whether and how often precision medicine is definitively required. Methods: We reviewed parallel clinical trials with numerical primary endpoints published in 2004, 2007, 2010 and 2013. We collected the baseline and final standard deviations of the main outcome measure. We assessed homoscedasticity by comparing the variance of the primary endpoint between arms through the outcome variance ratio (treated to control group). Results: The review provided 208 articles with enough information to conduct the analysis. One out of five studies (n = 40, 19.2%) had statistically different variances between groups, implying a non-constant-effect. The adjusted point estimate of the mean outcome variance ratio (treated to control group) is 0.89 (95% CI 0.81 to 0.97). Conclusions: The mean variance ratio is significantly lower than 1 and the lower variance was found more often in the intervention group than in the control group, suggesting it is more usual for treated patients to be stable. This observed reduction in variance might also imply that there could be a subgroup of less ill patients who derive no benefit from treatment. This would require further study as to whether the treatment effect outweighs the side effects as well as the economic costs. We have shown that there are ways to analyze the apparently unobservable constant effect.

Does evidence support the high expectations placed in precision medicine? A bibliographic review

Background: Precision medicine is the Holy Grail of interventions that are tailored to a patient's individual characteristics. However, conventional clinical trials are designed to find differences in averages, and interpreting these differences depends on untestable assumptions. Although only an ideal, a constant effect of treatment would facilitate individual management. A direct consequence of a constant effect is that the variance of the outcome measure would be the same in the treated and control arms. We reviewed the literature to explore the similarity of these variances as a foundation for examining whether and how often precision medicine is definitively required. Methods: We reviewed parallel clinical trials with numerical primary endpoints published in 2004, 2007, 2010 and 2013. We collected the baseline and final standard deviations of the main outcome measure. We assessed homoscedasticity by comparing the variance of the primary endpoint between arms through the outcome variance ratio (treated to control group). Results: The review provided 208 articles with enough information to conduct the analysis. One out of five studies (n = 40, 19.2%) had statistically different variances between groups, implying a non-constant-effect. The adjusted point estimate of the mean outcome variance ratio (treated to control group) is 0.89 (95% CI 0.81 to 0.97). Conclusions: The mean variance ratio is significantly lower than 1 and the lower variance was found more often in the intervention group than in the control group, suggesting it is more usual for treated patients to be stable. This observed reduction in variance might also imply that there could be a subgroup of less ill patients who derive no benefit from treatment. This would require further study as to whether the treatment effect outweighs the side effects as well as the economic costs. We have shown that there are ways to analyze the apparently unobservable constant effect.

Does evidence support the high expectations placed in precision medicine? A bibliographic review

Background: Precision medicine is the Holy Grail of interventions that are tailored to a patient’s individual characteristics. However, conventional clinical trials are designed to find differences in averages, and interpreting these differences depends on untestable assumptions. Although only an ideal, a constant effect of treatment would facilitate individual management. A direct consequence of a constant effect is that the variance of the outcome measure would be the same in the treated and control arms. We reviewed the literature to explore the similarity of these variances as a foundation for examining whether and how often precision medicine is definitively required.

Methods: We reviewed parallel clinical trials with numerical primary endpoints published in 2004, 2007, 2010 and 2013. We collected the baseline and final standard deviations of the main outcome measure. We assessed homoscedasticity by comparing the variance of the primary endpoint between arms through the outcome variance ratio (treated to control group).

Results: The review provided 208 articles with enough information to conduct the analysis. One out of five studies (n = 40, 19.2%) had statistically different variances between groups, implying a non-constant-effect. The adjusted point estimate of the mean outcome variance ratio (treated to control group) is 0.89 (95% CI 0.81 to 0.97).

Conclusions: The mean variance ratio is significantly lower than 1 and the lower variance was found more often in the intervention group than in the control group, suggesting it is more usual for treated patients to be stable. This observed reduction in variance might also imply that there could be a subgroup of less ill patients who derive no benefit from treatment. This would require further study as to whether the treatment effect outweighs the side effects as well as the economic costs. We have shown that there are ways to analyze the apparently unobservable constant effect.

Does evidence support the high expectations placed in precision medicine? A bibliographic review

Background: Precision medicine is the Holy Grail of interventions that are tailored to a patient's individual characteristics. However, conventional clinical trials are designed to find differences in averages, and interpreting these differences depends on untestable assumptions. Although only an ideal, a constant effect of treatment would facilitate individual management. A direct consequence of a constant effect is that the variance of the outcome measure would be the same in the treated and control arms. We reviewed the literature to explore the similarity of these variances as a foundation for examining whether and how often precision medicine is definitively required. Methods: We reviewed parallel clinical trials with numerical primary endpoints published in 2004, 2007, 2010 and 2013. We collected the baseline and final standard deviations of the main outcome measure. We assessed homoscedasticity by comparing the variance of the primary endpoint between arms through the outcome variance ratio (treated to control group). Results: The review provided 208 articles with enough information to conduct the analysis. One out of five studies (n = 40, 19.2%) had statistically different variances between groups, implying a non-constant-effect. The adjusted point estimate of the mean outcome variance ratio (treated to control group) is 0.89 (95% CI 0.81 to 0.97). Conclusions: The mean variance ratio is significantly lower than 1 and the lower variance was found more often in the intervention group than in the control group, suggesting it is more usual for treated patients to be stable. This observed reduction in variance might also imply that there could be a subgroup of less ill patients who derive no benefit from treatment. This would require further study as to whether the treatment effect outweighs the side effects as well as the economic costs. We have shown that there are ways to analyze the apparently unobservable constant effect.

One by One: Accumulating Evidence by using Meta-Analytical Procedures for Single-Case Experiments

This paper presents a unilevel and multilevel approach for the analysis and meta-analysis of single-case experiments (SCEs). We propose a definition of SCEs and derive the specific features of SCEs’ data that have to be taken into account when analysing and meta-analysing SCEs. We discuss multilevel models of increasing complexity and propose alternative and complementary techniques based on probability combining and randomisation test wrapping. The proposed techniques are demonstrated with real-life data and corresponding R code.

Sample size considerations for n-of-1 trials

N-of-1 trials are trials in which patients are treated with two or more treatments on multiple occasions. They can have many different purposes and can be analysed in different frameworks. In this note, five different criteria for planning sample sizes for n-of-1 trials are identified, and formulae and advice to address the associated tasks are provided. The basic design addressed is that of randomisation to treatment and control within cycles of pairs of episodes and the model assumed is that of a Normal-Normal mixture with variance components corresponding to within-cycle within-patient variation and treatment-by-patient interaction. The code to accomplish the tasks has been written in GenStat®, SAS® and R® and the application of the approaches is illustrated.

Aggregated N-of-1 trials for unlicensed medicines for small populations: an assessment of a trial with ephedrine for myasthenia gravis

Background

Inexpensive medicines with a long history of use may currently be prescribed off-label for rare indications. Reimbursement is at the discretion of health insurance companies, and may be unpredictable. The example addressed was ephedrine as add-on treatment for myasthenia gravis. Stakeholders from academia, a patient organization, the Dutch National Health Care Institute (NHCI) and Dutch Medicines Evaluation Board (MEB) advised on the trial design. The NHCI and MEB agreed to provide scientific advice on the suitability of the evidence generated by the trial, for regulatory decisions. This paper describes the feasibility of the trial and the utility of its aggregated results.

Results

The trialists experienced the trial as feasible. Retrospective interviews showed that the trial as performed was acceptable to patients. The treatment effect in the primary outcome measure, muscle strength, was statistically significant when inferred to the population level, though the effect size was modest. Secondary outcomes were statistically significant in a preplanned, fixed effects analysis within the four patients. The NHCI advised that it could potentially make reimbursement decisions based on the Fitting Evidence framework, should the trialists decide to apply for reimbursement. The MEB advised that for a licensing decision, the N-of-1 design is a last-resort option for demonstrating treatment benefit in a rare disease. N-of-1 trials alone do not provide enough evidence on potential risk. The MEB found the current trial inconclusive. It suggested doing a 2-armed trial of longer duration, possibly with a different outcome measure (postponement of corticosteroid use). It suggested engaging a consultancy or commercial sponsor, should the trialists decide to seek market authorization of the drug.

Conclusions

In theory, evidence from aggregated N-of-1 trials is suitable for use in licensing and reimbursement decisions. The current example illustrates differences in interpretation of N-of-1 results by health authorities. In the era of personalized medicine, consensus is required on the interpretation of data from study designs geared to small groups. Demonstrating effectiveness of inexpensive medicines in small populations may require involvement of non-commercial parties, to preserve affordability.

Electronic supplementary material

The online version of this article (doi:10.1186/s13023-017-0636-y) contains supplementary material, which is available to authorized users.