What ever happened to N-of-1 trials? Insiders' perspectives and a look to the future

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

PURPOSE

To investigate opinions among healthcare stakeholders whether implementation of the N-of-1 trial approach in clinical practice is a feasible way to optimize evidence-based treatment results for unique patients.

METHODS

We interviewed clinicians, researchers, and a patient advocate (n = 13) with an interest in or experience with N-of-1 trials on the following topics: experience with N-of-1, measurement, validity and reliability, informally gathered data usability, and influence on physician-patient relationship. Interviews were analysed using qualitative, thematic analysis.

RESULTS

The N-of-1 approach has the potential to shift the current healthcare system towards embracing personalized medicine. However, its application in clinical practice carries significant challenges in terms of logistics, time investment and acceptability. New skills will be required from patients and healthcare providers, which may alter the patient-physician relationship. The rise of consumer technology enabling self-measurement may leverage the uptake of N-of-1 approaches in clinical practice.

CONCLUSIONS

There is a strong belief that the N-of-1 approach has the potential to play a prominent role in transitioning the current healthcare system towards embracing personalized medicine. However, there are many barriers deeply ingrained in our healthcare system that hamper the uptake of the N-of-1 approach, making it momentarily only interesting for research purposes.

The development of a set of key points to aid clinicians and researchers in designing and conducting n-of-1 trials

INTRODUCTION

n-of-1 trials are undertaken to optimise the evaluation of health technologies in individual patients. They involve a single patient receiving treatments, both interventional and control, consecutively over set periods of time, the order of which is decided at random. Although n-of-1 trials are undertaken in medical research it could be argued they have the utility to be undertaken more frequently. We undertook the National Institute for Health Research (NIHR) commissioned DIAMOND (Development of generalisable methodology for n-of-1 trials delivery for very low volume treatments) project to develop key points to assist clinicians and researchers in designing and conducting n-of-1 trials.

METHODS

The key points were developed by undertaking a stakeholder workshop, followed by a discussion within the study team and then a stakeholder dissemination and feedback event. The stakeholder workshop sought to gain the perspectives of a variety of stakeholders (including clinicians, researchers and patient representatives) on the design and use of n-of-1 trials. A discussion between the study team was held to reflect on the workshop and draft the key points. Lastly, the stakeholders from the workshop were invited to a dissemination and feedback session where the proposed key points were presented and their feedback gained.

RESULTS

A set of 22 key points were developed based on the insights from the workshop and subsequent discussions. They provide guidance on when an n-of-1 trial might be a viable or appropriate study design and discuss key decisions involved in the design of n-of-1 trials, including determining an appropriate number of treatment periods and cycles, the choice of comparator, recommended approaches to randomisation and blinding, the use of washout periods and approaches to analysis.

CONCLUSIONS

The key points developed in the project will support clinical researchers to understand key considerations when designing n-of-1 trials. It is hoped they will support the wider implementation of the study design.

Protocol for randomized personalized trial for stress management compared to standard of care

Stress is a significant public health burden in the United States, with most Americans reporting unhealthy levels of stress. Stress management techniques include various evidence-based treatments shown to be effective but with heterogeneous treatment responses, indicating a lack of uniform benefits for all individuals. Designed to assess a participant's response to a specific intervention, personalized (N-of-1) trials provide guidance for which treatment (s) work (s) best for the individual. Prior studies examining the effects of mindfulness meditation, yoga, and walking for stress reduction found all three interventions to be associated with significant reductions in self-reported measures of stress. Delivering these treatments using a personalized trial approach has the potential to assist clinicians in identifying the best stress management techniques for individuals with persistently high stress while fostering treatment decisions that consider their personal condition/barriers. This trial will evaluate a personalized approach compared to standard of care for three interventions (guided mindfulness meditation; guided yoga; and guided brisk walking) to manage perceived stress. Participants will respond to daily surveys and wear a Fitbit device for 18 weeks. After a 2-week baseline period, participants in the personalized trial groups will receive 12 weeks of interventions in randomized order, while participants in the standard-of-care group will have access to all interventions for self-directed stress management. After intervention, all participants will undergo 2 weeks of observation, followed by two additional weeks of the stress management intervention of their choosing while continuing outcome measurement. At study completion, all participants will be sent a satisfaction survey. The primary analysis will compare perceived stress levels between the personalized and standard of care arms. The results of this trial will provide further support for the use of personalized designs for managing stress. Clinical Trial Registration: clinicaltrials.gov, NCT05408832. Protocol version: 9/14/2022, 21-0968-MRB.

A Series of Remote Melatonin Supplement Interventions for Poor Sleep: Protocol for a Feasibility Pilot Study for a Series of Personalized (N-of-1) Trials

BACKGROUND

Poor sleep, defined as short-duration or poor-quality sleep, is a frequently reported condition with many deleterious effects including poorer cognitive functioning, increased accidents, and poorer health. Melatonin has been shown to be an efficacious treatment to manage symptoms of poor sleep. However, the treatment effects of melatonin on sleep can vary greatly between participants. Personalized, or N-of-1, trial designs represent a method for identifying the best treatment for individual participants. Although using N-of-1 trials of melatonin to treat poor sleep is possible, the feasibility, acceptability, and effectiveness of N-of-1 trials using melatonin are unknown. Using the National Institutes of Health Stage Model for Behavioral Intervention Development, a stage IB (intervention refinement, modification, and adaptation and pilot testing) design appeared to be needed to address these feasibility questions.

OBJECTIVE

This trial series evaluates the feasibility, acceptability, and effectiveness of a series of personalized interventions for remote delivery of melatonin dose (3 and 0.5 mg) versus placebo supplements for self-reported poor sleep among 60 participants. The goal of this study is to provide valuable information about implementing remote N-of-1 randomized controlled trials to improve poor sleep.

METHODS

Participants will complete a 2-week baseline followed by six 2-week alternating intervention periods of 3 mg of melatonin, 0.5 mg of melatonin, and placebo. Participants will be randomly assigned to 2 intervention orders. The feasibility and acceptability of the personalized trial approach will be determined with participants' ratings of usability and satisfaction with the remote, personalized intervention delivery system. The effectiveness of the intervention will be measured using participants' self-reported sleep quality and duration and Fitbit tracker-measured sleep duration and efficiency. Additional measures will include ecological momentary assessment measures of fatigue, stress, pain, mood, concentration, and confidence as well as measures of participant adherence to the intervention, use of the Fitbit tracker, and survey data collection.

RESULTS

As of the submission of this protocol, recruitment for this National Institutes of Health stage IB personalized trial series is approximately 78.3% complete (47/60). We expect recruitment and data collection to be finalized by June 2023.

CONCLUSIONS

Evaluating the feasibility, acceptability, and effectiveness of a series of personalized interventions of melatonin will address the longer term aim of this program of research-is integrating N-of-1 trials useful patient care? The personalized trial series results will be published in a peer-reviewed journal and will follow the CONSORT (Consolidated Standards of Reporting Trials) extension for N-of-1 trials (CENT 2015) reporting guidelines. This trial series was approved by the Northwell Health institutional review board.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05349188; https://www.clinicaltrials.gov/study/NCT05349188.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45313.

Protocol of a feasibility study of a virtual personalized (N-of-1) trial for increasing low-intensity physical activity in older adults via habit formation

Background

Personalized interventions that can be delivered remotely are needed to increase physical activity (PA) in older adults to reduce risk of CV disease and mortality. Prior research indicates that Behavioral Change Techniques (BCTs) (e.g., goal setting, self-monitoring, behavioral repetition) can instill a habit for increasing daily walking. However, past interventions relied on between-subject randomized clinical trials, which can only only be informative about response of the hypothetical average person. Personalized trial designs can identify the benefits of an intervention for a specific individual although extended periods are required for collecting frequent measurements within-subject. Advances in remote, virtual technologies (e.g., text messaging, activity trackers), integrated with automatic platforms, can meet these requirements because they capacitate delivery of BCT interventions, and collection of data during daily life without personal contact. This Stage I-b trial is designed test whether a virtual, personalized intervention is feasible and acceptable to older adults, can elicit participant adherence and exhibit preliminary evidence for efficacy.

Methods

A series of up to 60 single-arm, personalized trials, involving no personal contact, will recruit adults, 45-75 years of age, to wear an activity tracker during a 2-week baseline and a 10-week intervention. Five BCT prompts to execute a walking plan will be delivered on a daily basis during the intervention phase. Participants will rate satisfaction with personalized trial components and whether automaticity of the walking plan can be achieved. Step-counts, adherence to the walking plan and self-monitoring of step-count will also be recorded.

Series of virtual light therapy interventions for fatigue: a feasibility pilot study protocol for a series of personalised (N-of-1) trials

INTRODUCTION

Fatigue is one of the most commonly recorded patient symptoms that can result in deficits in aspects of psychomotor functioning, cognition, work performance and mood. Research shows that bright light and dim light therapy may be an efficacious way to reduce symptoms of fatigue. Still, the feasibility, scalability, individual treatment effects and adverse event heterogeneity of these treatments are unknown.

METHODS AND ANALYSIS

The current study evaluates the feasibility, acceptability and effectiveness of a series of personalised (N-of-1) interventions for virtual delivery of bright light therapy and dim light therapy versus usual care treatment for fatigue in 60 participants. We hypothesise that this study will provide valuable information about implementing virtual, N-of-1 randomised controlled trials (RCTs) for fatigue. It will also offer results about determining participants' ratings of usability and satisfaction with the virtual, personalised intervention delivery system; evaluating participants' improvement of fatigue symptoms; and, in the long term, identify ways to integrate N-of-1 light therapy trials into patient care.

ETHICS AND DISSEMINATION

This trial was approved by the Northwell Health Institutional Review Board. The trial results will be published in a peer-reviewed journal. All publications resulting from this series of personalised trials will follow the Consolidated Standards of Reporting Trials extension for N-of-1 trials CENT 2015 reporting guidelines.

REGISTRATION DETAILS

This trial is registered in www.

CLINICALTRIALS

gov (number NCT04707846).

TRIAL REGISTRATION NUMBER

NCT04707846.

Personalized Feedback for Personalized Trials: Construction of Summary Reports for Participants in a Series of Personalized Trials for Chronic Lower Back Pain

Personalized (N-of-1) trials offer a patient-centered research approach that can provide important clinical information for patients when selecting which treatment options best manage their chronic health concern. Researchers utilizing this approach should present trial results to patients in a clear and understandable manner in order for personalized research trials to be useful to participants. The current study provides participant feedback examples for personalized trial reports using lay summaries and multiple presentation styles from a series of 60 randomized personalized trials examining the effects of massage and yoga versus usual care on chronic lower back pain (CLBP). Researchers generated summary participant reports that describe individual participant results using multiple presentation modalities of data (e.g., visual, written, and auditory) to offer the most appealing style for various participants. The article discusses contents of the participant report as well as participant satisfaction with the personalized summary report, captured using a satisfaction survey administered after study completion. The results from the satisfaction survey in the current study show that participants were generally satisfied with their personalized summary report. Researchers will use feedback from the participants in the current study to refine personalized feedback reports for future studies.

Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review

Emotional disorders are the most common mental disorders globally. Psychological treatments have been found to be useful for a significant number of cases, but up to 40% of patients do not respond to psychotherapy as expected. Artificial intelligence (AI) methods might enhance psychotherapy by providing therapists and patients with real- or close to real-time recommendations according to the patient’s response to treatment. The goal of this investigation is to systematically review the evidence on the use of AI-based methods to enhance outcomes in psychological interventions in real-time or close to real-time. The search included studies indexed in the electronic databases Scopus, Pubmed, Web of Science, and Cochrane Library. The terms used for the electronic search included variations of the words “psychotherapy”, “artificial intelligence”, and “emotional disorders”. From the 85 full texts assessed, only 10 studies met our eligibility criteria. In these, the most frequently used AI technique was conversational AI agents, which are chatbots based on software that can be accessed online with a computer or a smartphone. Overall, the reviewed investigations indicated significant positive consequences of using AI to enhance psychotherapy and reduce clinical symptomatology. Additionally, most studies reported high satisfaction, engagement, and retention rates when implementing AI to enhance psychotherapy in real- or close to real-time. Despite the potential of AI to make interventions more flexible and tailored to patients’ needs, more methodologically robust studies are needed.

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.

Methylphenidate for attention-deficit/hyperactivity disorder in patients with Smith-Magenis syndrome: protocol for a series of N-of-1 trials

Background

Smith–Magenis syndrome (SMS) is a rare genetic neurodevelopmental disorder characterized by intellectual disability and severe behavioural and sleep disturbances. Often, patients with SMS are diagnosed with attention-deficit/hyperactivity disorder (ADHD). However, the effectiveness of methylphenidate (MPH), the first-line pharmacological treatment for ADHD, in patients with SMS is unclear. Our objective is to examine the effectiveness of MPH for ADHD symptoms in individuals with SMS, proposing an alternative trial design as traditional randomized controlled trials are complex in these rare and heterogeneous patient populations.

Methods and analysis

We will initiate an N-of-1 series of double-blind randomized and placebo-controlled multiple crossover trials in six patients aged ≥ 6 years with a genetically confirmed SMS diagnosis and a multidisciplinary established ADHD diagnosis, according to a power analysis based on a summary measures analysis of the treatment effect. Each N-of-1 trial consists of a baseline period, dose titration phase, three cycles each including randomized intervention, placebo and washout periods, and follow-up. The intervention includes twice daily MPH (doses based on age and body weight). The primary outcome measure will be the subscale hyperactivity/inattention of the Strengths and Difficulties Questionnaire (SDQ), rated daily. Secondary outcome measures are the shortened version of the Emotion Dysregulation Inventory (EDI) reactivity index, Goal Attainment Scaling (GAS), and the personal questionnaire (PQ). Statistical analysis will include a mixed model analysis. All subjects will receive an assessment of their individual treatment effect and data will be aggregated to investigate the effectiveness of MPH for ADHD in SMS at a population level.

Conclusions

This study will provide information on the effectiveness of MPH for ADHD in SMS, incorporating personalized outcome measures. This protocol presents the first properly powered N-of-1 study in a rare genetic neurodevelopmental disorder, providing a much-needed bridge between science and practice to optimize evidence-based and personalized care.

Trial registration

This study is registered in the Netherlands Trial Register (NTR9125).

Establishment of an International Collaborative Network for N-of-1 Trials and Single-Case Designs

In this article we briefly examine the unique features of Single-Case Designs (SCDs) (studies in a single participant), their history and current trends, and real-world clinical applications. The International Collaborative Network for N-of-1 Trials and Single-Case Designs (ICN) is a formal collaborative network for individuals with an interest in SCDs. The ICN was established in 2017 to support the SCD scientific community and provide opportunities for collaboration, a global communication channel, resource sharing and knowledge exchange. In May 2021, there were more than 420 members in 31 countries. A member survey was undertaken in 2019 to identify priorities for the ICN for the following few years. This article outlines the key priorities identified and the ICN's progress to date in these key areas including network activities (developing a communications strategy to increase awareness, collecting/sharing a comprehensive set of resources, guidelines and tips, and incorporating the consumer perspective) and scientific activities (writing position papers and guest editing special journal issues, exploring key stakeholder perspectives about SCDs, and working to streamline ethical approval processes for SCDs). The ICN provides a practical means to engage with this methodology through membership. We encourage clinicians, researchers, industry, and healthcare consumers to learn more about and conduct SCDs, and to join us in our mission of using SCDs to improve health outcomes for individuals and populations.

Racial and ethnic minority patient participation in N-of-1 trials: perspectives of healthcare providers and patients

Aim: Patients from racial and ethnic minority backgrounds in the USA have historically been under-represented in research trials. Understanding their viewpoints regarding participation in N-of-1 trials is imperative as we design and implement these studies. Materials & methods: We conducted six focus groups of racial and ethnic minority patients (n = 25) and providers (n = 9). We used content analysis to identify themes. Results: Our results noted the importance of considering family members in N-of-1 trial recruitment and participation, patients' desire for education as a design feature, for 'lifestyle' changes as a treatment option and for use of nonevidence-based treatments in the design of future N-of-1 trials. Conclusion: Personalized trials have the potential to change the way we deliver primary care and improve disparities for minorities.

Experimental Designs to Optimize Treatments for Individuals: Personalized N-of-1 Trials

Conventional randomized clinical trials (RCTs) compare treatment effectiveness to provide support for evidence-based treatments that can be generalized to the average patient. However, the information obtained from RCTs may not always be useful for selecting the best treatment for individual patients. This article presents a complementary approach to identifying optimized treatments using experimental designs that focus on individuals. Personalized, or N-of-1, designs provide both a comparative analysis of treatments and a functional analysis demonstrating that changes in patient symptoms are likely because of the treatment implemented. This approach contributes to the zeitgeist of personalized medicine and provides clinicians with a paradigm for investigating optimal treatments for rare diseases for which RCTs are not always feasible, identifying personally effective treatments for patients with comorbidities who have historically been excluded from most RCTs, handling clinical situations in which patients respond idiosyncratically (either positively or negatively) to treatment, and shortening the time lag between identification and implementation of an evidence-based treatment. These designs merge experimental analysis of behavior methods used for decades in psychology with new methodological and statistical advances to assess significance levels of changes in individual patients, and they can be generalized to larger populations for meta-analytic purposes. This article presents a case for why these models are needed, an overview of how to apply personalized designs for different types of clinical scenarios, and a brief discussion of challenges associated with interpretation and implementation of personalized designs. The goal is to empower pediatricians to take personalized trial designs into clinical practice to identify optimal treatments for their patients.

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.

Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey

OBJECTIVE

To describe individual patient preferences for Personalised Trials and to identify factors and conditions associated with patient preferences.

DESIGN

Each participant was presented with 18 conjoint questions via an online survey. Each question provided two choices of Personalised Trials that were defined by up to eight attributes, including treatment types, clinician involvement, study logistics and trial burden on a patient.

SETTING

Online survey of adults with at least two common chronic conditions in the USA.

PARTICIPANTS

A nationally representative sample of 501 individuals were recruited from the Chronic Illness Panel by Harris Poll Online. Participants were recruited from several sources, including emails, social media and telephone recruitment of the target population.

MAIN OUTCOME MEASURES

The choice of Personalised Trial design that the participant preferred with each conjoint question.

RESULTS

There was large variability in participants' preferences for the design of Personalised Trials. On average, they preferred certain attributes, such as a short time commitment and no cost. Notably, a population-level analysis correctly predicted 62% of the conjoint responses. An empirical Bayesian analysis of the conjoint data, which supported the estimation of individual-level preferences, improved the accuracy to 86%. Based on estimates of individual-level preferences, patients with chronic pain preferred a long study duration (p≤0.001). Asthma patients were less averse to participation burden in terms of data-collection frequency than patients with other conditions (p=0.002). Patients with hypertension were more cost-sensitive (p<0.001).

CONCLUSION

These analyses provide a framework for elucidating individual-level preferences when implementing novel patient-centred interventions. The data showed that patient preference in Personalised Trials is highly variable, suggesting that individual differences must be accounted for when marketing Personalised Trials. These results have implications for advancing precise interventions in Personalised Trials by indicating when rigorous scientific principles, such as frequent monitoring, is feasible in a substantial subset of patients.

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.

Factors Associated With Trial Completion and Adherence in App-Based N-of-1 Trials: Protocol for a Randomized Trial Evaluating Study Duration, Notification Level, and Meaningful Engagement in the Brain Boost Study

Background

N-of-1 trials promise to help individuals make more informed decisions about treatment selection through structured experiments that compare treatment effectiveness by alternating treatments and measuring their impacts in a single individual. We created a digital platform that automates the design, administration, and analysis of N-of-1 trials. Our first N-of-1 trial, the app-based Brain Boost Study, invited individuals to compare the impacts of two commonly consumed substances (caffeine and L-theanine) on their cognitive performance.

Objective

The purpose of this study is to evaluate critical factors that may impact the completion of N-of-1 trials to inform the design of future app-based N-of-1 trials. We will measure study completion rates for participants that begin the Brain Boost Study and assess their associations with study duration (5, 15, or 27 days) and notification level (light or moderate).

Methods

Participants will be randomized into three study durations and two notification levels. To sufficiently power the study, a minimum of 640 individuals must begin the study, and 97 individuals must complete the study. We will use a multiple logistic regression model to discern whether the study length and notification level are associated with the rate of study completion. For each group, we will also compare participant adherence and the proportion of trials that yield statistically meaningful results.

Results

We completed the beta testing of the N1 app on a convenience sample of users. The Brain Boost Study on the N1 app opened enrollment to the public in October 2019. More than 30 participants enrolled in the first month.

Conclusions

To our knowledge, this will be the first study to rigorously evaluate critical factors associated with study completion in the context of app-based N-of-1 trials.

Trial Registration

ClinicalTrials.gov NCT04056650; https://clinicaltrials.gov/ct2/show/NCT04056650

International Registered Report Identifier (IRRID)

PRR1-10.2196/16362

Exploring the Potential for Collaborative Use of an App-Based Platform for n-of-1 Trials Among Healthcare Professionals That Treat Patients With Insomnia

BACKGROUND

N-of-1 trials are single patient, multiple crossover, and comparative effectiveness experiments. Despite their rating as "level 1" evidence, they are not routinely used in clinical medicine to evaluate the effectiveness of treatments.

OBJECTIVE

We explored the potential for implementing a mobile app-based n-of-1 trial platform for collaborative use by clinicians and patients to support data-driven decisions around the treatment of insomnia.

METHODS

A survey assessing awareness and utilization of n-of-1 trials was administered to healthcare professionals that frequently treat patients with insomnia at the Icahn School of Medicine at Mount Sinai in New York City.

RESULTS

A total of 45 healthcare professionals completed the survey and were included in the analysis. We found that 64% (29/45) of healthcare professionals surveyed had not heard of n-of-1 trials. After a brief description of these methods, 75% (30/40) of healthcare professionals reported that they are likely or highly likely to use an app-based n-of-1 trial at least once in the next year if the service were free and easy to offer to their patients.

CONCLUSIONS

An app-based n-of-1 trials platform might be a valuable tool for clinicians and patients to identify the best treatments for insomnia. The lack of awareness of n-of-1 trials coupled with receptivity to their use suggests that educational interventions may address a current barrier to wider utilization of n-of-1 trials.

A Randomized Placebo-Controlled N-of-1 Trial: The Effect of Proton Pump Inhibitor in the Management of Gastroesophageal Reflux Disease

Background

Gastroesophageal reflux disease (GERD) is the most frequent chronic gastrointestinal disorder. It is defined as a condition developed when the reflux of gastric contents causes troublesome symptoms (heartburn and regurgitation). This requires adequate treatment since it can lead to long-term complications including esophagus adenocarcinoma. Proton pump inhibitors (PPI) are generally used to treat GERD due to their high-security profile and efficiency on most patients. However, recurrent reflux despite initial treatment is frequent. N-of-1 trial is a study that allows the identification of the best treatment for each patient. The objective of this study is to compare the efficacy of standard dose with double dosage of esomeprazole, to improve the GERD symptoms in a single patient.

Methods

A single-patient trial, placebo-controlled, randomized, double-blind, was carried out from September 25th, 2012, to April 26th, 2013. It included one outpatient at the gastroenterology service in a fourth-level hospital, diagnosed with nonerosive reflux disease (NERD). Yet, his symptoms were heartburn and reflux, and his endoscopic results were normal esophageal mucosa, without hiatal hernia, though pathological pH values. A no-obese male without any tobacco or alcohol usage received esomeprazole 40 mg/day and 40 mg/bid for 24 weeks. A standardized gastroesophageal reflux disease questionnaire (GerdQ) was used weekly to evaluate symptom frequency and severity. The consumption of 90% of the capsules was considered as an adequate treatment adherence. D'agostino-Pearson and Wilcoxon test were used to determine normal or nonnormal distribution and compare both treatments, respectively, both with a significant statistical difference of p < 0.05.

Results

The patient completed the study with 96% of adherence. The double dosage of esomeprazole did not improve the control of symptoms compared with the standard dosage. Mean symptomatic score was 9.5±0.5 and 10.2±0.6 for each treatment, respectively (p > 0.05).

Conclusion

There was no significant improvement in the patient GERD symptoms increasing the dose of oral esomeprazole during the 6 months of study. N-of-1 trials in chronic pathologies including GERD are recommended due to their potential value as systematic methods that evaluate therapies without strong scientific evidence.

Evaluating the Comparative Effectiveness of Two Diets in Pediatric Inflammatory Bowel Disease: A Study Protocol for a Series of N-of-1 Trials

Inflammatory bowel disease (IBD) affects 3 million children and adults in the US. Treatment involves medications with considerable risk profiles. Dietary modification, such as the specific carbohydrate diet (SCD), may be helpful in treating IBD, but there is insufficient evidence of its effectiveness. N-of-1 trials are ideal for addressing this important research question. The Personalized Research on Diet in Ulcerative Colitis and Crohn’s Disease (PRODUCE) study employs a series of 50 individual N-of-1 trials that compare the SCD to a modified SCD. Treatment periods are assigned in blocks of two, with each patient completing two balanced treatment blocks. Patients are randomized to start with the SCD or modified SCD and alternate between conditions for four eight-week periods. A mobile app guides collecting and viewing data, transitioning diets, and reviewing personal results. Primary outcomes include patient reported outcomes (PROs) of stool frequency, stool consistency, pain interference, and gastrointestinal (GI) symptom severity. We examine changes in inflammation via fecal calprotectin. Participants will receive a personalized answer regarding comparative effectiveness between the SCD and a less restrictive diet option (modified SCD), as well as compared to their baseline diet. We will aggregate the results of completed N-of-1 trials across patients to estimate population level comparative effectiveness of these treatments and the effectiveness of each diet.

Acceptability and barriers to implementation of N-of-1 tests in Ethiopia - a qualitative study

Background

Locally produced generic drugs offer a cost–effective alternative to imported drugs to treat patients in Ethiopia. However, due to a lack of bioequivalence testing, additional assurance tests are needed to build trust in cheaper, locally made drugs. By testing bioequivalence of local drugs to gold standard, N-of-1 tests have the potential to promote patient centred quality use of medicines.

Method

We sought to assess the acceptability of, and explore barriers to, conducting N-of-1 tests to evaluate local medicines in a resource limited clinical setting. We conducted a descriptive qualitative study, analysing four focus group discussions and five key informant interviews. Participants were senior drug regulatory authority members, institutional review board members, physicians and patients. All interviews were audio taped and transcribed verbatim. Patient interviews were conducted in Amharic and translated to English prior to analysis. Data analysis used an inductive, thematic process.

Results

Five major themes were identified; (1) Appropriateness of N-of-1 tests to determine the therapeutic equivalence of local drugs, (2) N-of-1 therapeutic equivalence tests: clinical care or research? (3) Ethical and regulatory requirements (IRB), (4) Potential barriers to implementing N-of-1 tests and (5) Possible solutions to identified challenges. The study demonstrated considerable support for using N-of-1 tests for clinical equivalence studies between local and imported medicines, but important impediments were very likely to impact the feasibility of conducting N-of-1 tests in Ethiopia. Key informants from the regulatory authority did not support additional tests of local drugs. There were also mixed opinions regarding ethical requirements for conducting N-of-1 tests. The Institutional Review Board (IRB) members believed that IRB approval was sufficient to conduct N-of-1 tests, however, the regulatory authority members considered that N-of-1 tests constituted a clinical trial, and required approval at the regulatory level.

Conclusion

This study showed that there were key uncertainties that could impact the feasiblity of using N-of-1 testing local drugs in Ethiopia. Therefore, a number of protocol amendments to address contextual threats and regulatory challenges, would be needed before progressing to conducting these tests.

Increasing the Precision of Hypertension Treatment Through Personalized Trials: a Pilot Study

BACKGROUND

There are substantial differences in the effects of blood pressure (BP) medications in individual patients. Yet, the current standard approach to prescribing BP medications is not personalized.

OBJECTIVE

To determine the feasibility of individualizing the selection of BP medications through pragmatic personalized (i.e., N-of-1) trials.

DESIGN

Series of N-of-1 trials.

SETTING

Outpatient.

PATIENTS

Hypertensive adults prescribed none or one BP medication.

INTERVENTION

Participation in a flexible, open-label personalized trial of two to three BP medications (NCT02744456).

MEASUREMENTS

BP was measured twice per day with a validated home BP device. Frequency and severity of side effects were assessed at the end of the day via an electronic questionnaire. Patients' BP medication preference was assessed after reviewing BP lowering and side effect results with a study clinician. Feasibility was assessed by determining the proportion of patients who adhered to self-assessments. Benefit was assessed by asking patients to rate the helpfulness of participation and whether they would recommend personalized trials to other hypertensive patients.

KEY RESULTS

Of ten patients enrolled, two dropped out prior to initiation, one discovered white coat hypertension through ambulatory BP monitoring, and seven (mean age 58 years, 71% of women) completed personalized trials. All seven were compliant with home BP monitoring and side effect tracking. All seven recommended personalized trials of BP medications to others. Thiazides were preferred by three patients, renin-angiotensin system-blocking agents by two patients, a combination of thiazide and beta-blocker by one patient, and any of three classes by one patient.

CONCLUSIONS

Personalized trials of BP medications were feasible and led to improved treatment precision. Heterogeneity of patient preferences and of therapeutic BP response for first-line BP medications can be determined through a personalized trial approach.

Designing Robust N-of-1 Studies for Precision Medicine: Simulation Study and Design Recommendations

Background

Recent advances in molecular biology, sensors, and digital medicine have led to an explosion of products and services for high-resolution monitoring of individual health. The N-of-1 study has emerged as an important methodological tool for harnessing these new data sources, enabling researchers to compare the effectiveness of health interventions at the level of a single individual.

Objective

N-of-1 studies are susceptible to several design flaws. We developed a model that generates realistic data for N-of-1 studies to enable researchers to optimize study designs in advance.

Methods

Our stochastic time-series model simulates an N-of-1 study, incorporating all study-relevant effects, such as carryover and wash-in effects, as well as various sources of noise. The model can be used to produce realistic simulated data for a near-infinite number of N-of-1 study designs, treatment profiles, and patient characteristics.

Results

Using simulation, we demonstrate how the number of treatment blocks, ordering of treatments within blocks, duration of each treatment, and sampling frequency affect our ability to detect true differences in treatment efficacy. We provide a set of recommendations for study designs on the basis of treatment, outcomes, and instrument parameters, and make our simulation software publicly available for use by the precision medicine community.

Conclusions

Simulation can facilitate rapid optimization of N-of-1 study designs and increase the likelihood of study success while minimizing participant burden.

Treating Hypertension in Children With n-of-1 Trials

OBJECTIVES

Clinicians prescribe antihypertensive medication to children with primary hypertension, but without data to define a particular choice as first-line therapy. A one-size-fits-all approach may not be appropriate for these patients. Our aim was to develop a personalized approach to hypertension treatment, using repeated ambulatory blood pressure monitoring (ABPM) in n-of-1 trials (single-patient randomized crossover trials).

METHODS

Children undergoing hypertension management at a single pediatric referral center were offered participation in an n-of-1 trial with repeated ABPM to compare 3 commonly used medications. The medication producing the greatest blood pressure reduction, and without unacceptable side effects, was selected as the preferred therapy for the individual.

RESULTS

Forty-two children agreed to participate; 7 were normotensive without medication; and 3 failed to complete one treatment cycle. Of the remaining 32 patients, lisinopril was preferred for 16, amlodipine for 8, hydrochlorothiazide for 4, and 4 had uncontrolled blood pressure on maximum doses of monotherapy. In conservative Bayesian analyses, the proportion of patients who preferred lisinopril was 49% (95% credible interval [CrI]: 32% to 69%), 24% (95% CrI: 12% to 41%) preferred amlodipine, and 12% (95% CrI: 4% to 26%) preferred hydrochlorothiazide. The preferred therapy for the majority (67%) of African American participants was lisinopril. Unacceptable side effects were reported in 24% of assessments for hydrochlorothiazide, 16% for lisinopril, and 13% for amlodipine.

CONCLUSIONS

No single medication was preferred for more than half of hypertensive children. n of-1 trials with repeated ABPM may promote better informed and individualized decisions in pediatric hypertension management.

Time Series Visualizations of Mobile Phone-Based Daily Diary Reports of Stress, Physical Activity, and Diet Quality in Mostly Ethnic Minority Mothers: Feasibility Study

Background

Health behavior patterns reported through daily diary data are important to understand and intervene upon at the individual level in N-of-1 trials and related study designs. There is often interest in relationships between multiple outcomes, such as stress and health behavior. However, analyses often utilize regressions that evaluate aggregate effects across individuals, and standard analyses target single outcomes.

Objective

This paper aims to illustrate how individuals’ daily reports of stress and health behavior (time series) can be explored using visualization tools.

Methods

Secondary analysis was conducted on 6 months of daily diary reports of stress and health behavior (physical activity and diet quality) from mostly ethnic minority mothers who pilot-tested a self-monitoring mobile health app. Time series with minimal missing data from 14 of the 44 mothers were analyzed. Correlations between stress and health behavior within each time series were reported as a preliminary step. Stress and health behavior time series patterns were visualized by plotting moving averages and time points where mean shifts in the data occurred (changepoints).

Results

Median correlation was small and negative for associations of stress with physical activity (r=−.14) and diet quality (r=−.08). Moving averages and changepoints for stress and health behavior were aligned for some participants but not for others. A third subset of participants exhibited little variation in stress and health behavior reports.

Conclusions

Median correlations in this study corroborate prior findings. In addition, time series visualizations highlighted variations in stress and health behavior across individuals and time points, which are difficult to capture through correlations and regression-based summary measures.

Patient Perceptions of Their Own Data in mHealth Technology-Enabled N-of-1 Trials for Chronic Pain: Qualitative Study

Background

N-of-1 (individual comparison) trials are a promising approach for comparing the effectiveness of 2 or more treatments for individual patients; yet, few studies have qualitatively examined how patients use and make sense of their own patient-generated health data (PGHD) in the context of N-of-1 trials.

Objective

The objective of our study was to explore chronic pain patients’ perceptions about the PGHD they compiled while comparing 2 chronic pain treatments and tracking their symptoms using a smartphone N-of-1 app in collaboration with their clinicians.

Methods

Semistructured interviews were recorded with 33 patients, a consecutive subset of the intervention group in a primary study testing the feasibility and effectiveness of the Trialist N-of-1 app. Interviews were transcribed verbatim, and a descriptive thematic analysis was completed.

Results

Patients were enthusiastic about recording and accessing their own data. They valued sharing data with clinicians but also used their data independently.

Conclusions

N-of-1 trials remain a promising approach to evidence-based decision making. Patients appear to value their roles as trial participants but place as much or more importance on the independent use of trial data as on comparative effectiveness results. Future efforts to design patient-centered N-of-1 trials might consider adaptable designs that maximize patient flexibility and autonomy while preserving a collaborative role with clinicians and researchers.

Effect of Mobile Device-Supported Single-Patient Multi-crossover Trials on Treatment of Chronic Musculoskeletal Pain: A Randomized Clinical Trial

IMPORTANCE

Individually designed single-patient multi-crossover (n-of-1) trials can facilitate tailoring of treatments directed at various conditions, including chronic musculoskeletal pain (CMSP) but are potentially burdensome, which may limit uptake in research and practice.

OBJECTIVES

To determine whether patients randomized to participate in an n-of-1 trial supported by a mobile health (mHealth) app would experience less pain and improved global health, adherence, satisfaction, and shared decision making compared with patients assigned to usual care.

DESIGN, SETTING, AND PARTICIPANTS

This randomized clinical trial compared participation in an individualized, mHealth-supported n-of-1 trial vs usual care. The participating 215 patients had CMSP for at least 6 weeks, had a smartphone or tablet with a data plan, were enrolled in northern California from July 2014 through July 2016, and were followed for up to 1 year by 48 clinicians in academic, community, Veterans Affairs, and military settings.

INTERVENTIONS

Intervention patients met with their clinicians and used a desktop interface to select treatments and trial parameters for an n-of-1 trial comparing 2 pain-management regimens. The mHealth app provided reminders to take designated treatments on assigned days and to upload responses to daily questions on pain and treatment-associated adverse effects. Control patients received care as usual.

MAIN OUTCOMES AND MEASURES

The primary outcome was change in the PROMIS (Patient-Reported Outcomes Measurement Information System) pain-related interference 8-item short-form scale (full scale range, 41-78) from baseline to 6 months. Secondary outcomes included patient-reported pain intensity, overall health, analgesic adherence, trust in clinician, satisfaction with care, medication-related shared decision making, and, for the n-of-1 group only, participant engagement and experience.

RESULTS

Among 215 patients (108 randomized to the n-of-1 intervention and 107 to control), 102 (47%) were women, and the mean (SD) age was 55.5 (11.1) years. At the 6-month follow-up, pain interference was reduced in both groups, though there was no difference between the intervention and control groups (-1.36 points; 95% CI, -2.91 to 0.19 points; P = .09). There were no advantages in secondary outcomes for intervention patients vs control patients except for higher medication-related shared decision making at 6 months (between-group difference, 11.9 points; 95% CI, 2.6-21.2 points; P = .01). Among patients assigned to the n-of-1 group, 88% (n = 86) affirmed that the mHealth app could help people like them manage their pain.

CONCLUSIONS AND RELEVANCE

In this population of patients with CMSP, mHealth-supported n-of-1 trials were feasible and associated with a satisfactory user experience, but n-of-1 trial participation did not significantly improve pain interference at 6 months vs usual care.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02116621.

Using "Big Data" in the Cost-Effectiveness Analysis of Next-Generation Sequencing Technologies: Challenges and Potential Solutions

Next-generation sequencing (NGS) is considered to be a prominent example of "big data" because of the quantity and complexity of data it produces and because it presents an opportunity to use powerful information sources that could reduce clinical and health economic uncertainty at a patient level. One obstacle to translating NGS into routine health care has been a lack of clinical trials evaluating NGS technologies, which could be used to populate cost-effectiveness analyses (CEAs). A key question is whether big data can be used to partially support CEAs of NGS. This question has been brought into sharp focus with the creation of large national sequencing initiatives. In this article we summarize the main methodological and practical challenges of using big data as an input into CEAs of NGS. Our focus is on the challenges of using large observational datasets and cohort studies and linking these data to the genomic information obtained from NGS, as is being pursued in the conduct of large genomic sequencing initiatives. We propose potential solutions to these key challenges. We conclude that the use of genomic big data to support and inform CEAs of NGS technologies holds great promise. Nevertheless, health economists face substantial challenges when using these data and must be cognizant of them before big data can be confidently used to produce evidence on the cost-effectiveness of NGS.

Personalized (N-of-1) Trials for Depression: A Systematic Review

PURPOSE/BACKGROUND

Personalized (N-of-1) trials are single-patient, crossover-design trials that may be useful for personalizing the selection of depression treatments. We conducted a systematic review of published N-of-1 trials for depression to determine the feasibility and suitability of this methodology for personalizing depression care.

METHODS/PROCEDURES

Electronic databases were searched from database inception through October 2016. Studies were selected if they enrolled depressed patients, included a within-subject crossover design, and systematically assessed depressive symptoms during the N-of-1 trial.

FINDINGS/RESULTS

Five eligible studies reporting on 47 depressed patients (range, 1-18 patients) were identified. Two studies were conducted among adults with treatment-resistant depression, 1 study among depressed inpatients, and 2 studies among patients from special populations (geriatric nursing home, human immunodeficiency virus-associated encephalopathy). All studies evaluated the effects of pharmacologic treatments (methylphenidate, D-amphetamine, ketamine, and sulpiride). Three studies compared an off-label treatment with placebo, 1 study compared 2 off-label treatments, and 1 study compared escalating doses of an off-label treatment with placebo. All 4 studies with more than 1 participant demonstrated heterogeneous treatment effects. All studies produced data that could personalize treatment selection for individual patients. No studies reported on recruitment challenges, compliance with self-tracking, or satisfaction with participation.

IMPLICATIONS/CONCLUSIONS

The feasibility of N-of-1 trials for depression was demonstrated for a limited number of second-line pharmacologic treatments in treatment-resistant patients or in patients with comorbidities that would have excluded them from conventional randomized controlled trials. Additional research is needed to determine whether N-of-1 trials are suitable for improving the selection of depression treatments in clinical practice.

Patient preferences for personalized (N-of-1) trials: a conjoint analysis

OBJECTIVE

Despite their promise for increasing treatment precision, Personalized Trials (i.e., N-of-1 trials) have not been widely adopted. We aimed to ascertain patient preferences for Personalized Trials.

STUDY DESIGN AND SETTING

We recruited 501 adults with ≥2 common chronic conditions from Harris Poll Online. We used Sawtooth Software to generate 45 plausible Personalized Trial designs comprising combinations of eight key attributes (treatment selection, treatment type, clinician involvement, blinding, time commitment, self-monitoring frequency, duration, and cost) at different levels. Conditional logistic regression was used to assess relative importance of different attributes using a random utility maximization model.

RESULTS

Overall, participants preferred Personalized Trials with no costs vs. $100 cost (utility difference 1.52 [standard error 0.07], P < 0.001) and with less vs. more time commitment/day (0.16 [0.07], P < 0.015) but did not hold preferences for the other six attributes. In subgroup analyses, participants ≥65 years, white, and with income ≤$50,000 were more averse to costs than their counterparts (P all <0.05).

CONCLUSION

To optimize dissemination, Personalized Trial designers should seek to minimize out-of-pocket costs and time burden of self-monitoring. They should also consider adaptive designs that can accommodate subgroup differences in design preferences.

N-of-1 trials in the clinical care of patients in developing countries: a systematic review

BACKGROUND

N-of-1 trials have a potential role in promoting patient-centered medicine in developing countries. However, there is limited academic literature regarding the use of N-of-1 trials in the clinical care of patients in resource-poor settings.

OBJECTIVE

To assess the extent of use, purpose and treatment outcome of N-of-1 trials in developing countries.

METHOD

A systematic review of clinical N-of-1 trials was conducted between 1985 and September 2015 using PubMed, Embase, CINAHL, Web of Science and the Cochrane Central Register of Controlled Trials. Grey literature databases and clinical trial registers were also searched. This review included randomized, multi-cycle, crossover within individual patient trials involving drug intervention. Quality assessment and data extraction were conducted by two independent reviewers.

RESULT

Out of 131 N-of-1 trials identified, only 6 (4.5%) were conducted in developing countries. The major reason that N-of-1 trials were used was to provide evidence on feasibility, effectiveness and safety of therapies. A total of 72 participants were involved in these trials. Five of the studies were conducted in China and all evaluated Chinese traditional medicine. The remaining study was conducted in Brazil. The completion rate was 93%. More than half, 46 (69%) of subjects made medication changes consistent with trial results after trial completion. A number of threats to the validity of the included evidence limited the validity of the evidence. In particular, the estimated overall effect in four of the included studies could have been affected by the "carry over" of the previous treatment effect as no adequate pharmacokinetic evidence regarding traditional medicines was presented.

CONCLUSION

The prevalence and scope of N-of-1 trials in developing countries is low. A coordinated effort among government, clinicians, researchers and sponsor organizations is needed to increase their uptake and quality in developing countries.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42015026841 .

Evaluating the Impact of Physical Activity Apps and Wearables: Interdisciplinary Review

Background

Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work.

Objectives

This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed.

Method

An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis.

Results

A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance).

Conclusions

The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines.

Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials

Many of an individual's historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, regularly monitored clinical events or chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-effect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an N1OS association as a causal effect (e.g., as if randomization had occurred) is needed.Our goal in this paper is to help bridge the methodological gap between risk-factor discovery and N1RT testing by introducing a basic counterfactual framework for N1OS design and personalized causal analysis.We introduce and characterize what we call the average period treatment effect (APTE), i.e., the estimand of interest in an N1RT, and build an analytical framework around it that can accommodate autocorrelation and time trends in the outcome, effect carryover from previous treatment periods, and slow onset or decay of the effect. The APTE is loosely defined as a contrast (e.g., difference, ratio) of averages of potential outcomes the individual can theoretically experience under different treatment levels during a given treatment period. To illustrate the utility of our framework for APTE discovery and estimation, two common causal inference methods are specified within the N1OS context. We then apply the framework and methods to search for estimable and interpretable APTEs using six years of the author's self-tracked weight and exercise data, and report both the preliminary findings and the challenges we faced in conducting N1OS causal discovery.Causal analysis of an individual's time series data can be facilitated by an N1RT counterfactual framework. However, for inference to be valid, the veracity of certain key assumptions must be assessed critically, and the hypothesized causal models must be interpretable and meaningful.

Protocol for a systematic review of N-of-1 trial protocol guidelines and protocol reporting guidelines

BACKGROUND

N-of-1 trials are multiple cross-over trials done in individual participants, generating individual treatment effect information. While reporting guidelines for the CONSORT Extension for N-of-1 trials (CENT) and the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) already exist, there is no standardized recommendation for the reporting of N-of-1 trial protocols.

OBJECTIVE

The objective of this study is to evaluate current literature on N-of-1 design and reporting to identify key elements of rigorous N-of-1 protocol design.

METHODS

We will conduct a systematic search for all N-of-1 trial guidelines and protocol-reporting guidelines published in peer-reviewed literature. We will search Medline, Embase, PsycINFO, CINAHL, the Cochrane Methodology Register, CENTRAL, and the NHS Economic Evaluation Database. Eligible articles will contain explicit guidance on N-of-1 protocol construction or reporting. Two reviewers will independently screen all titles and abstracts and then undertake full-text reviews of potential articles to determine eligibility. One reviewer will perform data extraction of selected articles, checked by the second reviewer. Data analysis will ascertain common features of N-of-1 trial protocols and compare them to the SPIRIT and CENT items.

DISCUSSION

This systematic review assesses recommendations on the design and reporting of N-of-1 trial protocols. These findings will inform an international Delphi development process for an N-of-1 trial protocol reporting guideline. The development of this guideline is critical for improving the quality of N-of-1 protocols, leading to improvements in the quality of published N-of-1 trial research.

Pragmatic medicine in solid cancer: a translational alternative to precision medicine

The precision medicine (PM) initiative is a response to the dismal outlook in solid cancer. Despite heterogeneity, common mechanistic denominators may exist across the spectrum of solid cancer. A shift from conventional research and development (R&D) toward PM will require conceptual and structural change. As individuals and as a society, we welcome innovation, but question change. We ask: In solid cancer, does PM identify and address the causes of prior failures, and, if so, are the proposed solutions feasible? And, when may we expect safer, more effective and affordable drugs in the clinic? Considerations that prompt a pragmatic rethink include a failure analysis of translational R&D in solid cancer suggesting that trials and regulations need to be aligned with the natural history of the disease. In successful therapeutic interventions in chronic, complex disease, surrogate markers and endpoints should be consistent with the Prentice's criteria. In solid cancer, drug induced tumor shrinkage, is a drug effect and not a disease response; tumor shrinkage does not reflect nor predict interruption of the disease. Overall, we support a pragmatic, multidisciplinary, and collaborative R&D, and suggest that direction be set by clinical need and utility, and by questions, not answers. PM will prove worthwhile if it could improve clinical outcomes. The lag in therapeutics relative to diagnostics is a cause for confusion. Overdiagnosis adds to fear and harm, especially in the absence of effective interventions. A revised initiative that prioritizes metastasis research could replicate the successful HIV/AIDS model in solid cancer. A pragmatic approach may further translational efforts toward meaningfully effective, generally available, and affordable solutions.

Reporting of core items in hierarchical Bayesian analysis for aggregating N-of-1 trials to estimate population treatment effects is suboptimal

OBJECTIVES

N-of-1 trials can be aggregated to estimate population treatment effects using hierarchical Bayesian models. It is very important to report core items in hierarchical Bayesian analysis. In this study, we assessed reporting of items in hierarchical Bayesian analysis for aggregating N-of-1 trials to estimate population treatment effects.

STUDY DESIGN AND SETTING

This was a systematic literature review of aggregating N-of-1 trials by hierarchical Bayesian models to estimate population treatment effects. A comprehensive search was performed to collect eligible articles. Pilot studies, formal N-of-1 trials and reports in which the data were reanalyzed using hierarchical Bayesian methods, were included. The information of reported items related with hierarchical Bayesian analysis was extracted by two independent reviewers. The guideline "ROBUST," developed for reporting Bayesian analysis of clinical studies, was published in Journal of Clinical Epidemiology in 2005. We assessed the included reports using ROBUST criteria and 18 other important items.

RESULTS

After careful screening, 11 studies were identified to be eligible for inclusion. There were three pilot studies, four formal trials, and four reports in which the data were reanalyzed using hierarchical Bayesian methods. The number of reported items in ROBUST criteria ranged from six to seven, with a median number of six. Five of eleven included articles reported all items of the ROBUST criteria. But for justification and sensitivity analysis in prior distribution items, other items were reported in all of the included articles. Software and analysis data set items were reported the most frequently in additional items excluded from the ROBUST criteria. Less than half of the studies reported the other additional items.

CONCLUSION

Reporting of core items in hierarchical Bayesian analysis for aggregating N-of-1 trials to estimate population treatment effects is suboptimal. A PRISMA-like guidance on reviews of Bayesian N-of-1 trials may be required in the future.

Issues in assessing products for the treatment of hemophilia - the intersection between efficacy, economics, and ethics

Following the obviation of the pathogen safety threats posed by previous generations of clotting factor concentrates for the treatment of hemophilia, the principal issue facing the patient community is timely access to adequate supplies of continuously improving therapies. The application of evidence-based medicine has enhanced the basis of hemophilia therapy, while resulting in some challenges to patient care. Increasingly, the criteria used for the approval and payment of treatment products by regulatory and reimbursement agencies, respectively, are becoming inflexible and unrealistic. This is occurring particularly in the requirements for demonstrating product efficacy. Concurrently, emerging evidence of the interpatient variability in the clinical response to therapy has led to the proposed personalization of therapeutic regimens. Possible impediments to optimal care include competitive tensions among suppliers who seek to gain label claims for reimbursement purposes, which result in clinical trial designs of, arguably, unethical design, carried out in poor countries. We synthesize these converging developments to suggest some changes to the current hemophilia treatment paradigm, which should make it more patient-centric and enable speedier access to new therapies.

Practice to research: integrating evidence-based practices with culture and context

There are ways to integrate culturally competent services (CCS) and evidence-based practices (EBP) which can improve the experiences of patients and their families and communities when faced with health problems, as well as the effectiveness and positive experiences of practitioners. CCS and EBP evidence should be jointly deployed for helping patients and clinicians. Partnership research models are useful for achieving the integration of CCS and EBP, since they involve close observation of and participation by clinicians and practitioners in the research process, and often use integrated qualitative and quantitative mixed methods. We illustrate this with 3 examples of work that can help integrate CCS and EBP: ongoing collection of information from patients, clinicians and staff, or "evidence farming"; close study and continuous improvement of activities and accommodations; and use of evidence of tacit, implicit cultural scripts and norms, such as being "productive," as well as explicit scripts. From a research practice point of view, collaborative partnerships will likely produce research with culture and context bracketed in, and will contribute stronger research models, methods, and units of analysis.

The PREEMPT study - evaluating smartphone-assisted n-of-1 trials in patients with chronic pain: study protocol for a randomized controlled trial

Background

Chronic pain is prevalent, costly, and clinically vexatious. Clinicians typically use a trial-and-error approach to treatment selection. Repeated crossover trials in a single patient (n-of-1 trials) may provide greater therapeutic precision. N-of-1 trials are the most direct way to estimate individual treatment effects and are useful in comparing the effectiveness and toxicity of different analgesic regimens. The goal of the PREEMPT study is to test the ‘Trialist’ mobile health smartphone app, which has been developed to make n-of-1 trials easier to accomplish, and to provide patients and clinicians with tools for individualizing treatments for chronic pain.

Methods/design

A randomized controlled trial is being conducted to test the feasibility and effectiveness of the Trialist app. A total of 244 participants will be randomized to either the Trialist app intervention group (122 patients) or a usual care control group (122 patients). Patients assigned to the Trialist app will work with their clinicians to set up an n-of-1 trial comparing two pain regimens, selected from a menu of flexible options. The Trialist app provides treatment reminders and collects data entered daily by the patient on pain levels and treatment side effects. Upon completion of the n-of-1 trial, patients review results with their clinicians and develop a long-term treatment plan. The primary study outcome (comparing Trialist to usual care patients) is pain-related interference with daily functioning at 26 weeks.

Discussion

Trialist will allow patients and clinicians to conduct personalized n-of-1 trials. In prior studies, n-of-1 trials have been shown to encourage greater patient involvement with care, which has in turn been associated with better health outcomes. mHealth technology implemented using smartphones may offer an efficient means of facilitating n-of-1 trials so that more patients can benefit from this approach.

Trial registration

ClinicalTrials.gov: NCT02116621, first registered 15 April 2014.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-015-0590-8) contains supplementary material, which is available to authorized users.

Joint WFH-ISTH session: issues in clinical trial design

Haemophilia therapy is experiencing an unprecedented expansion in the number and novelty of clotting factor concentrates. Every product must be licensed by regulatory authorities, primarily on the basis of its safety and efficacy profiles. The low prevalence of haemophilia, and other inherited bleeding disorders, presents a significant challenge to patient recruitment for preauthorization clinical trials, especially given the low frequency of inhibitory antibodies, the major adverse event related to clotting factor exposure. Other challenges include a lack of harmonization between the major regulatory authorities in certain key areas, the selection of laboratory monitoring methodologies and the difficulty in obtaining high-quality phase IV safety data following authorization. These aspects will be reviewed in this session, which will also highlight the roles played by the World Federation of Hemophilia and International Society on Thrombosis and Haemostasis in the promotion of these discussions.