Aggregating single patient (n-of-1) trials in populations where recruitment and retention was difficult: The case of palliative care

Nikles, Jane; Mitchell, G.; Schluter, Philip J.; Good, Phillip; Hardy, Janet; Rowett, Debra; Shelby‐James, Tania; Vohra, Sunita; Currow, David C.

doi:10.1016/j.jclinepi.2010.05.009

Cited by 78 publications

(99 citation statements)

References 37 publications

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“…patient selected outcomes, use of subjective outcome measures like patient diaries, and seeking the preferences of patients) or tailoring of drug treatment to a specific individual by using pre-trial dose finding. These examples were identified by reading through the key N of 1 papers [3,15,16]. …”

Section: Aimsmentioning

confidence: 99%

N of 1 trials and the optimal individualisation of drug treatments: a systematic review protocol

et al. 2017

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Background: Guidelines and evidence-based drug treatment recommendations are usually based on the results of clinical trials, which have limited generalisability in routine clinical settings due to their restrictive eligibility criteria. These trials are also conducted in ideal and rigorously controlled settings. N of 1 trials, which are single patient multiple crossover studies, offer a means of increasing the evidence base and individualising care for individuals in clinical practice. This systematic review of the N of 1 drug treatment trial aims to investigate its usefulness for achieving optimal individualised patient care. Methods:The following databases will be searched for relevant articles: MEDLINE, EMBASE, PsycINFO (all via Ovid), AMED, CINAHAL (via EBSCO), The Cochrane Library (including CENTRAL, NHS EED, and DARE), and Web of Science (Thomson Reuters). Supplementary searches will include ongoing trial databases and organisational websites. All N of 1 trials in which patients have been treated with a drug will be considered. Outcomes will include information on the clinical usefulness of N of 1 trials-i.e. achievement of optimal individualised care, health-care utilisation of patients, frequently used practices, experiences of clinical care or participation in N of 1 trials, adherence to treatment plan, and unwanted effects of the treatment. Screening of included papers will be undertaken independently by two reviewers, while data extraction and the quality of reporting will be conducted by one reviewer and checked by another. Both quantitative and qualitative summaries will be reported using appropriate methods. Discussion: This review will provide new insights into the clinical utility of N of 1 drug trials in helping participants find the most acceptable treatment as defined by patients and clinicians based on the selected outcome measures and the perspectives of participants involved in such trials. Findings from this review will inform the development of a stakeholder workshop and guidance to help physicians find the optimum therapy for their patients and will help guide future research on N of 1 trials. Systematic review registration: PROSPERO CRD42016032452

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Section: Aimsmentioning

confidence: 99%

N of 1 trials and the optimal individualisation of drug treatments: a systematic review protocol

et al. 2017

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“…Sidman first described this design in the psychological literature in 1960 (Sidman 1960). Since then, N-of-1 trials have been used in a variety of disciplines (Nikles et al 2011). The first N-of-1 trials were multicycle, doubleblind, and controlled crossover trials using standardized measures of effect.…”

Section: Genetic and Epigenetic Variability: Molecular Phenotypingmentioning

confidence: 99%

The genomics of micronutrient requirements

2015

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Healthy nutrition is accepted as a cornerstone of public health strategies for reducing the risk of noncommunicable conditions such as obesity, cardiovascular disease, and related morbidities. However, many research studies continue to focus on single or at most a few factors that may elicit a metabolic effect. These reductionist approaches resulted in: (1) exaggerated claims for nutrition as a cure or prevention of disease; (2) the wide use of empirically based dietary regimens, as if one fits all; and (3) frequent disappointment of consumers, patients, and healthcare providers about the real impact nutrition can make on medicine and health. Multiple factors including environment, host and microbiome genetics, social context, the chemical form of the nutrient, its (bio)availability, and chemical and metabolic interactions among nutrients all interact to result in nutrient requirement and in health outcomes. Advances in laboratory methodologies, especially in analytical and separation techniques, are making the chemical dissection of foods and their availability in physiological tissues possible in an unprecedented manner. These omics technologies have opened opportunities for extending knowledge of micronutrients and of their metabolic and endocrine roles. While these technologies are crucial, more holistic approaches to the analysis of physiology and environment, novel experimental designs, and more sophisticated computational methods are needed to advance our understanding of how nutrition influences health of individuals.

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“…Strategies for analyses of individual data (often called n-of-1 data [35]) for identifying homeostatic groups and metabolic response groups (e. g., [19,38]) may circumvent the limitations of case-control experimental designs. In n-of-1 designs, individuals are analyzed completely, or as completely as possible, and then analytical methods are used to fi nd those of like response or characteristics (Figure 1).…”

Section: N-of-1 System Biologymentioning

confidence: 99%

“…From a translational perspective, these response groups will most likely help to inform optimal dietary interventions on an individual-specifi c basis. However there is more work to be done in terms of applying appropriate study designs and analytical strategies in order to understand nutritional systems.Our approaches uses a cyclic method of analyzing a subset of individuals ( Figure 1A) and classifying them ( Figure 1B), This approach allows the same data sets to be aggregated and used for analysis of population level results [35]. A distinct challenge for this experimental design is the level of data necessary to characterize the system.…”

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confidence: 99%

Discovery-Based Nutritional Systems Biology: Developing N-of-1 Nutrigenomic Research

Kaput

Morine

2012

International Journal for Vitamin and Nutrition Research

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Biological and biomedical research, like many established human activities, is based on conventions and practices that become standardized over time. Many of these conventions are decades old and do not account for the great advances in knowledge resulting from modern research. Some of the main practices of biomedical research are discussed herein, in an attempt to initiate a dialogue in the nutrition community about new concepts for personalized nutrition and health. The Design of ExperimentsThe design of biomedical research experiments rests on standards developed almost 100 years ago. The methodology was codifi ed in RA Fisher's The Design of Experiments [12] a 1935 treatise that described the key elements of good research protocols. These benchmarks are a valued requisite for determining the quality of experimental results and Fisher's experimental design is now considered the gold standard for human Abstract: The progress in and success of biomedical research over the past century was built on the foundation outlined in R.A. Fisher's The Design of Experiments (1935), which described the theory and methodological approach to designing research studies. A key tenet of Fisher's treatise, widely adopted by the research community, is randomization, the process of assigning individuals to random groups or treatments. Comparing outcomes or responses between these groups yields "risk factors" called population attributable risks (PAR), which are statistical estimates of the percentage reduction in disease if the risk were avoided or in the case of genetic associations, if the gene variant were not present in the population . High throughput metabolomics, proteomic and genomic technologies provide 21 st century data that humans cannot be randomized into groups: individuals are genetically and biochemically distinct. Gene -environment interactions caused by unique dietary and lifestyle factors contribute to heterogeneity in physiologies observed in human studies. The risk factors determined for populations (i.e., PAR) cannot be applied to the individual. Developing individual risk or benefi t factors in light of the genetic diversity of human populations, the complexity of foods, culture and lifestyle, and the variety of metabolic processes that lead to health or disease are signifi cant challenges for personalizing dietary advice for healthy or medical treatments for individuals with chronic disease. Table I), when applied to the appropriate scientifi c question, produce reliable evidence for improving public and personal health. More humans survive childbirth, infancy, childhood, and through advanced age than any time in our species history [44].One of the key tenets of designing experiments is the requirement for randomization of subjects or other outbred animals to case and control groups. Randomization distributes unknown or unmeasurable characteristics between groups in an attempt to isolate and identify measurable variables that differ between or distinguish those groups. While this step was necessary in the ...

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Aggregating single patient (n-of-1) trials in populations where recruitment and retention was difficult: The case of palliative care

Cited by 78 publications

References 37 publications

N of 1 trials and the optimal individualisation of drug treatments: a systematic review protocol

N of 1 trials and the optimal individualisation of drug treatments: a systematic review protocol

The genomics of micronutrient requirements

Discovery-Based Nutritional Systems Biology: Developing N-of-1 Nutrigenomic Research

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