A comparison of four methods for the analysis of N-of-1 trials

Protocol for a personalized (N-of-1) trial for testing the effects of a mind-body intervention on sleep duration in middle-aged women working in health care

Background

Adequate sleep plays a crucial role in maintaining physical, mental, and emotional health. On average, adults require 7-9 h of sleep per night. However, less than two-thirds of women meet this recommendation. During the coronavirus disease 2019 (COVID-19) pandemic, poor sleep quality and moderate-to-severe stress were highly prevalent among healthcare workers (HCWs), especially women. While some interventions have been proposed to address stress/burnout in HCWs, few have focused specifically on women in healthcare. Therefore, this is a protocol for a study that aims to determine the efficacy of a mind-body intervention (MBI) to improve sleep duration among women HCWs aged 40-60 years using the personalized (N-of-1) trial design.

Methods

A personalized (N-of-1) trials model will be employed to evaluate the efficacy of an MBI to improve sleep duration (primary endpoint) and explore its effects on sleep quality, physiological factors, and their relationships with participants' perceived stress, anxiety, and depression. The series of personalized trials (n = 60) will be conducted over 16 weeks. The MBI will include mindfulness, yoga, and guided walking, delivered in two 2-week block sequences for 12 weeks, with two 2-week periods for baseline and follow-up. Participants will watch 30-min videos three times weekly and wear an activity tracker to monitor sleep and activity. They will receive daily text messages with questions about sleep quality and bi-weekly questionnaires about their stress, anxiety and depression scores, fatigue, concentration, confidence, mood, and pain levels.

Conclusion

Results from this study will inform the development of N-of-1 methodology for addressing the health and wellness needs of middle-aged women.

Single-case design meta-analyses in education and psychology: a systematic review of methodology

Meta-analysis is of increasing importance as this quantitative synthesis technique has the potential to summarize a tremendous amount of research evidence, which can help making evidence-based decisions in policy, practice, and theory. This paper examines the single-case meta-analyses within the Education and Psychology fields. The amount of methodological studies related to the meta-analysis of Single-Case Experimental Designs (SCEDs) is increasing rapidly, especially in these fields. This underscores the necessity of a succinct summary to help methodologists identify areas for further development in Education and Psychology research. It also aids applied researchers and research synthesists in discerning when to use meta-analytic techniques for SCED studies based on criteria such as bias, mean squared error, 95% confidence intervals, Type I error rates, and statistical power. Based on the summary of empirical evidence from 18 reports identified through a systematic search procedure, information related to meta-analytic techniques, data generation and analysis models, design conditions, statistical properties, conditions under which the meta-analytic technique is appropriate, and the study purpose(s) were extracted. The results indicate that three-level hierarchical linear modeling is the most empirically validated SCED meta-analytic technique, and parameter bias is the most prominent statistical property investigated. A large number of primary studies (more than 30) and at least 20 measurement occasions per participant are recommended for usage of SCED meta-analysis in Education and Psychology fields.

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.

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

BACKGROUND

The Net Benefit (Δ) is a measure of the benefit-risk balance in clinical trials, based on generalized pairwise comparisons (GPC) using several prioritized outcomes and thresholds of clinical relevance. We extended Δ to N-of-1 trials, with a focus on patient-level and population-level Δ.

METHODS

We developed a Δ estimator at the individual level as an extension of the stratum-specific Δ, and at the population-level as an extension of the stratified Δ. We performed a simulation study mimicking PROFIL, a series of 38 N-of-1 trials testing sildenafil in Raynaud's phenomenon, to assess the power for such an analysis with realistic data. We then reanalyzed PROFIL using GPC. This reanalysis was finally interpreted in the context of the main analysis of PROFIL which used Bayesian individual probabilities of efficacy.

RESULTS

Simulations under the null showed good size of the test for both individual and population levels. The test lacked power when being simulated from the true PROFIL data, even when increasing the number of repetitions up to 140 days per patient. PROFIL individual-level estimated Δ were well correlated with the probabilities of efficacy from the Bayesian analysis while showing similarly wide confidence intervals. Population-level estimated Δ was not significantly different from zero, consistently with the previous Bayesian analysis.

CONCLUSION

GPC can be used to estimate individual Δ which can then be aggregated in a meta-analytic way in N-of-1 trials. GPC ability to easily incorporate patient preferences allow for more personalized treatment evaluation, while needing much less computing time than Bayesian modeling.

StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials

N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice.

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.

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

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

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

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

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.

"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.

N-of-1 Clinical Trials in Nutritional Interventions Directed at Improving Cognitive Function

Longer life expectancy has led to an increase in the prevalence of age-related cognitive decline and dementia worldwide. Due to the current lack of effective treatment for these conditions, preventive strategies represent a research priority. A large body of evidence suggests that nutrition is involved in the pathogenesis of age-related cognitive decline, but also that it may play a critical role in slowing down its progression. At a population level, healthy dietary patterns interventions, such as the Mediterranean and the MIND diets, have been associated with improved cognitive performance and a decreased risk of neurodegenerative disease development. In the era of evidence-based medicine and patient-centered healthcare, personalized nutritional recommendations would offer a considerable opportunity in preventing cognitive decline progression. N-of-1 clinical trials have emerged as a fundamental design in evidence-based medicine. They consider each individual as the only unit of observation and intervention. The aggregation of series of N-of-1 clinical trials also enables population-level conclusions. This review provides a general view of the current scientific evidence regarding nutrition and cognitive decline, and critically states its limitations when translating results into the clinical practice. Furthermore, we suggest methodological strategies to develop N-of-1 clinical trials focused on nutrition and cognition in an older population. Finally, we evaluate the potential challenges that researchers may face when performing studies in precision nutrition and cognition.

Challenges and solutions for N-of-1 design studies in health psychology

Theories of behaviour change and health behaviour change interventions are most often evaluated in between-person designs. However, behaviour change theories apply to individuals not groups and behavioural interventions ultimately aim to achieve within-person rather than between-group change. Within-person methodology, such as N-of-1 (also known as single case design), can circumvent this issue, though has multiple design-specific challenges. This paper provides a conceptual review of the challenges and potential solutions for undertaking N-of-1 studies in health psychology. Key challenges identified include participant adherence to within-person protocols, carry-over and slow onset effects, suitability of behaviour change techniques for evaluation in N-of-1 experimental studies, optimal allocation sequencing and blinding, calculating power/sample size, and choosing the most suitable analysis approach. Key solutions include involving users in study design, employing recent technologies for unobtrusive data collection and problem solving by design. Within-person designs share common methodological requirements with conventional between-person designs but require specific methodological considerations. N-of-1 evaluation designs are appropriate for many though not all types of interventions. A greater understanding of patterns of behaviours and factors influencing behaviour change at the within-person level is required to progress health psychology into a precision science. Video abstract: Supplementary Material 1.

Parenting behaviors that shape child compliance: A multilevel meta-analysis

Background

What are the parenting behaviors that shape child compliance? Most research on parent-child interactions relies on correlational research or evaluations of “package deal” interventions that manipulate many aspects of parenting at the same time. Neither approach allows for identifying the specific parenting behaviors that shape child compliance. To overcome this, we systematically reviewed and meta-analyzed available evidence on the effects of experimentally manipulated, discrete parenting behaviors—a niche in parent-child interaction research that contributes unique information on the specific parenting behaviors that shape child behavior.

Methods

We identified studies by systematically searching databases and through contacting experts. Nineteen studies (75 effect sizes) on four discrete parenting behaviors were included: praise, verbal reprimands, time-out, and ignore. In multilevel models, we tested for each parenting behavior whether it increased child compliance, including both observed and parent-reported measures of child compliance.

Results

Providing “time-out” for noncompliance robustly increased both observed and parent-reported child compliance (ds = 0.84–1.72; 95% CI 0.30 to 2.54). The same holds for briefly ignoring the child after non-compliance (ds = 0.36–1.77; 95% CI 0.04 to 2.90). When observed and parent-reported outcomes were combined, but not when they were examined separately, verbal reprimands also increased child compliance (d = 0.72; 95% CI 0.26 to 1.19). Praise did not increase child compliance (ds = –0.27–1.19; 95% CI –2.04 to 1.59).

Conclusion

Our findings suggest that of the discrete parenting behaviors that are experimentally studied in multiple trials, especially time-out and ignore, and to some extent verbal reprimands, shape child compliance.

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.

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 .

Clinical and Vitamin Response to a Short-Term Multi-Micronutrient Intervention in Brazilian Children and Teens: From Population Data to Interindividual Responses

SCOPE

Micronutrients are in small amounts in foods, act in concert, and require variable amounts of time to see changes in health and risk for disease. These first principles are incorporated into an intervention study designed to develop new experimental strategies for setting target recommendations for food bioactives for populations and individuals.

METHODS AND RESULTS

A 6-week multivitamin/mineral intervention is conducted in 9-13 year olds. Participants (136) are (i) their own control (n-of-1); (ii) monitored for compliance; (iii) measured for 36 circulating vitamin forms, 30 clinical, anthropometric, and food intake parameters at baseline, post intervention, and following a 6-week washout; and (iv) had their ancestry accounted for as modifier of vitamin baseline or response. The same intervention is repeated the following year (135 participants). Most vitamins respond positively and many clinical parameters change in directions consistent with improved metabolic health to the intervention. Baseline levels of any metabolite predict its own response to the intervention. Elastic net penalized regression models are identified, and significantly predict response to intervention on the basis of multiple vitamin/clinical baseline measures.

CONCLUSIONS

The study design, computational methods, and results are a step toward developing recommendations for optimizing vitamin levels and health parameters for individuals.

Investigation into the Individualized Treatment of Traditional Chinese Medicine through a Series of N-of-1 Trials

PURPOSE

To compare the efficacy of individualized herbal decoction with standard decoction for patients with stable bronchiectasis through N-of-1 trials.

METHODS

We conducted a single center N-of-1 trials in 17 patients with stable bronchiectasis. Each N-of-1 trial contains three cycles. Each cycle is divided into two 4-week intervention including individualized decoction and fixed decoction (control). The primary outcome was patient self-reported symptoms scores on a 1-7 point Likert scale. Secondary outcomes were 24-hour sputum volume and CAT scores.

RESULTS

Among 14 completed trials, five showed that the individualized decoction was statistically better than the control decoction on symptom scores (P < 0.05) but was not clinically significant. The group data of all the trials showed that individualized decoction was superior to control decoction on symptom scores (2.13 ± 0.58 versus 2.30 ± 0.65, P = 0.002, mean difference and 95% CI: 0.18 (0.10, 0.25)), 24 h sputum volume (P = 0.009), and CAT scores (9.69 ± 4.89 versus 11.64 ± 5.59, P = 0.013, mean difference and 95% CI: 1.95 (1.04, 2.86)) but not clinically significant.

CONCLUSION

Optimizing the combined analysis of individual and group data and the improvement of statistical models may make contribution in establishing a method of evaluating clinical efficacy in line with the characteristics of traditional Chinese medicine individual diagnosis and treatment.

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.

Single-Subject Studies in Translational Nutrition Research

There is a great deal of interest in personalized, individualized, or precision interventions for disease and health-risk mitigation. This is as true of nutrition-based intervention and prevention strategies as it is for pharmacotherapies and pharmaceutical-oriented prevention strategies. Essentially, technological breakthroughs have enabled researchers to probe an individual's unique genetic, biochemical, physiological, behavioral, and exposure profile, allowing them to identify very specific and often nuanced factors that an individual might possess, which may make it more or less likely that he or she responds favorably to a particular intervention (e.g., nutrient supplementation) or disease prevention strategy (e.g., specific diet). However, as compelling and intuitive as personalized nutrition might be in the current era in which data-intensive biomedical characterization of individuals is possible, appropriately and objectively vetting personalized nutrition strategies is not trivial and requires novel study designs and data analytical methods. These designs and methods must consider a very integrated use of the multiple contemporary biomedical assays and technologies that motivate them, which adds to their complexity. Single-subject or N-of-1 trials can be used to assess the utility of personalized interventions and, in addition, can be crafted in such a way as to accommodate the necessarily integrated use of many emerging biomedical technologies and assays. In this review, we consider the motivation, design, and implementation of N-of-1 trials in translational nutrition research that are meant to assess the utility of personalized nutritional strategies. We provide a number of example studies, discuss appropriate analytical methods given the complex data they generate and require, and consider how such studies could leverage integration of various biomarker assays and clinical end points. Importantly, we also consider the development of strategies and algorithms for matching nutritional needs to individual biomedical profiles and the issues surrounding them. Finally, we discuss the limitations of personalized nutrition studies, possible extensions of N-of-1 nutritional intervention studies, and areas of future research.

Propelling the paradigm shift from reductionism to systems nutrition

The complex physiology of living organisms represents a challenge for mechanistic understanding of the action of dietary bioactives in the human body and of their possible role in health and disease. Animal, cell, and microbial models have been extensively used to address questions that could not be pursued experimentally in humans, posing an additional level of complexity in translation of the results to healthy and diseased metabolism. The past few decades have witnessed a surge in development of increasingly sensitive molecular techniques and bioinformatic tools for storing, managing, and analyzing increasingly large datasets. Application of such powerful means to molecular nutrition research led to a major leap in study designs and experimental approaches yielding experimental data connecting dietary components to human health. Scientific journals bear major responsibilities in the advancement of science. As primary actors of dissemination to the scientific community, journals can impose rigid criteria for publishing only sound, reliable, and reproducible data. Journal policies are meant to guide potential authors to adopt the most updated standardization guidelines and shared best practices. Such policies evolve in parallel with the evolution of novel approaches and emerging challenges and therefore require constant updating. We highlight in this manuscript the major scientific issues that led to formulating new, updated journal policies for Genes & Nutrition, a journal which targets the growing field of nutritional systems biology interfacing personalized nutrition and preventive medicine, with the ultimate goal of promoting health and preventing or treating disease. We focus here on relevant issues requiring standardization in nutrition research. We also introduce new sections on human genetic variation and nutritional bioinformatics which follow the evolution of nutritional science into the twenty-first century.

Recent highlights of Chinese medicine for advanced lung cancer

Owing to its unique superiority in improving quality of life and prolonging survival time among advanced lung cancer patients, Chinese medicine (CM) has, in recent years, received increased attentions worldwide. We utilized a bibliometric statistical method based on MEDLINE/GoPubMed to conduct a comprehensive analysis of the current application status of CM in lung cancer, by including annual and accumulated publications, origin distribution of countries and journals, and keywords with a higher frequency score. Then the relevant clinical trials and mechanistic studies were systematically summarized within the field according to research types. We have raised potential problems and provided potentially useful reference information that could guide similar studies in the future. The basic experimental results are highly consistent with clinical trials, leading us to conclude that CM can offer better overall therapeutic benefits when used in combination with routine Western medicine for patients with advanced lung cancer.

Understanding Variation in Sets of N-of-1 Trials

A recent paper in this journal by Chen and Chen has used computer simulations to examine a number of approaches to analysing sets of n-of-1 trials. We have examined such designs using a more theoretical approach based on considering the purpose of analysis and the structure as regards randomisation that the design uses. We show that different purposes require different analyses and that these in turn may produce quite different results. Our approach to incorporating the randomisation employed when the purpose is to test a null hypothesis of strict equality of the treatment makes use of Nelder’s theory of general balance. However, where the purpose is to make inferences about the effects for individual patients, we show that a mixed model is needed. There are strong parallels to the difference between fixed and random effects meta-analyses and these are discussed.

On the translocation of bacteria and their lipopolysaccharides between blood and peripheral locations in chronic, inflammatory diseases: the central roles of LPS and LPS-induced cell death

We have recently highlighted (and added to) the considerable evidence that blood can contain dormant bacteria. By definition, such bacteria may be resuscitated (and thus proliferate). This may occur under conditions that lead to or exacerbate chronic, inflammatory diseases that are normally considered to lack a microbial component. Bacterial cell wall components, such as the endotoxin lipopolysaccharide (LPS) of Gram-negative strains, are well known as potent inflammatory agents, but should normally be cleared. Thus, their continuing production and replenishment from dormant bacterial reservoirs provides an easy explanation for the continuing, low-grade inflammation (and inflammatory cytokine production) that is characteristic of many such diseases. Although experimental conditions and determinants have varied considerably between investigators, we summarise the evidence that in a great many circumstances LPS can play a central role in all of these processes, including in particular cell death processes that permit translocation between the gut, blood and other tissues. Such localised cell death processes might also contribute strongly to the specific diseases of interest. The bacterial requirement for free iron explains the strong co-existence in these diseases of iron dysregulation, LPS production, and inflammation. Overall this analysis provides an integrative picture, with significant predictive power, that is able to link these processes via the centrality of a dormant blood microbiome that can resuscitate and shed cell wall components.