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      Understanding Variation in Sets of N-of-1 Trials

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      Author(s): 1 , 2 , 1 , *
      Editor(s):
      Publication date (Electronic):
      Journal: PLoS ONE
      Publisher: Public Library of Science

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          Abstract

          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.

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          Single-patient (n-of-1) trials: a pragmatic clinical decision methodology for patient-centered comparative effectiveness research.

          Naihua Duan, Richard Kravitz, Christopher H Schmid (2013)
          To raise awareness among clinicians and epidemiologists that single-patient (n-of-1) trials are potentially useful for informing personalized treatment decisions for patients with chronic conditions. We reviewed the clinical and statistical literature on methods and applications of single-patient trials and then critically evaluated the needs for further methodological developments. Existing literature reports application of 2,154 single-patient trials in 108 studies for diverse clinical conditions; various recent commentaries advocate for wider application of such trials in clinical decision making. Preliminary evidence from several recent pilot acceptability studies suggests that single-patient trials have the potential for widespread acceptance by patients and clinicians as an effective modality for increasing the therapeutic precision. Bayesian and adaptive statistical methods hold promise for increasing the informational yield of single-patient trials while reducing participant burden, but are not widely used. Personalized applications of single-patient trials can be enhanced through further development and application of methodologies on adaptive trial design, stopping rules, network meta-analysis, washout methods, and methods for communicating trial findings to patients and clinicians. Single-patient trials may be poised to emerge as an important part of the methodological armamentarium for comparative effectiveness research and patient-centered outcomes research. By permitting direct estimation of individual treatment effects, they can facilitate finely graded individualized care, enhance therapeutic precision, improve patient outcomes, and reduce costs. Copyright © 2013 Elsevier Inc. All rights reserved.
          Article 0 comments Cited 81 times – based on 0 reviews     Review now
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            Individual (N-of-1) trials can be combined to give population comparative treatment effect estimates: methodologic considerations.

            Jeremy D. Zucker, Christopher H Schmid, Robin Ruthazer (2010)
            To compare different statistical models for combining N-of-1 trials to estimate a population treatment effect. Data from a published series of N-of-1 trials comparing amitriptyline (AMT) therapy and combination treatment (AMT+fluoxetine [FL]) were analyzed to compare summary and individual participant data meta-analysis; repeated-measure models; Bayesian hierarchical models; and single-period, single-pair, and averaged outcome crossover models. The best-fitting model included a random intercept (response on AMT) and fixed treatment effect (added FL). Results supported a common, uncorrelated within-patient covariance structure that is equal between treatments and across patients. Assuming unequal within-patient variances, a random-effect model was favored. Bayesian hierarchical models improved precision and were highly sensitive to within-patient variance priors. Optimal models for combining N-of-1 trials need to consider goals, data sources, and relative within- and between-patient variances. Without sufficient patients, between-patient variation will be hard to explain with covariates. N-of-1 data with few observations per patients may not support models with heterogeneous within-patient variation. With common variances, models appear robust. Bayesian models may improve parameter estimation but are sensitive to prior assumptions about variance components. With limited resources, improving within-patient precision must be balanced by increased participants to explain population variation. Copyright © 2010 Elsevier Inc. All rights reserved.
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              Hypothesis: comparisons of inter- and intra-individual variations can substitute for twin studies in drug research.

              W Kalow, Catherine S K Tang, L Endrenyi (1998)
              Twin studies are useful devices to determine the heritability of persistent but variable characteristics that tend to differ among individuals. Drug responses are not persistent affairs; they are temporary characteristics. One therefore may ask whether twin studies are necessary to assess the genetic element in pharmacological responsiveness. To measure the genetic component contributing to their variability, it seems logical to investigate the response variation by repeated drug administration to given individuals, and to compare the variability of the responses within and between individuals. We attempt here to describe a theoretical background of this venture, and to show some results of the exercise. Potential sources of error or uncertainty are discussed.
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                Author and article information

                Contributors
                Yu-Kang Tu: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 1 December 2016
                Publication date Collection: 2016
                Volume: 11
                Issue: 12
                Electronic Location Identifier: e0167167
                Affiliations
                [1 ]Competence Center for Methodology and Statistics, Luxembourg Institute of Health, Strassen, Luxembourg
                [2 ]Medical Statistics Group, School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
                National Taiwan University, TAIWAN
                Author notes

                Competing Interests: SS acts as a consultant to the pharmaceutical industry. As far as he aware, however, there is no conflict of interest. He maintains a full declaration of interests here http://www.senns.demon.co.uk/Declaration_Interest.htm.

                • Conceptualization: SS.

                • Data curation: SS.

                • Formal analysis: AA SS.

                • Funding acquisition: SS.

                • Investigation: SS AA SJ.

                • Methodology: SS.

                • Project administration: SS.

                • Resources: SS.

                • Software: AA SS.

                • Supervision: SS SJ.

                • Validation: SS AA.

                • Visualization: AA SS.

                • Writing – original draft: SS.

                • Writing – review & editing: SS AA SJ.

                Author information
                http://orcid.org/0000-0002-7558-8473
                Article
                Publisher ID: PONE-D-16-20889
                DOI: 10.1371/journal.pone.0167167
                PMC ID: 5131970
                PubMed ID: 27907056
                SO-VID: 4db58475-bbeb-47a2-97b3-3d420d1a7272
                Copyright © © 2016 Araujo et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 24 May 2016
                Date accepted : 9 November 2016
                Page count
                Figures: 3, Tables: 8, Pages: 24
                Funding
                Funded by: EU FP7
                Award ID: 602552
                Award Recipient :
                Funded by: Boehringer Ingelheim (UK)
                Award Recipient :
                Funded by: EU FP7
                Award ID: 602552
                Award Recipient :
                ORCID: http://orcid.org/0000-0002-7558-8473
                SS and AA’s work was funded by the European Union’s FP7 programme grant number 602552 for the IDEAL project. AA has received additional funding from Boehringer-Ingelheim. The support of both funders is gratefully acknowledged The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Research and Analysis Methods
                Subject: Mathematical and Statistical Techniques
                Subject: Statistical Methods
                Subject: Meta-Analysis
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Statistics (Mathematics)
                Subject: Statistical Methods
                Subject: Meta-Analysis
                Subject: Research and Analysis Methods
                Subject: Simulation and Modeling
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Statistics (Mathematics)
                Subject: Confidence Intervals
                Subject: Research and Analysis Methods
                Subject: Mathematical and Statistical Techniques
                Subject: Statistical Methods
                Subject: Analysis of Variance
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Statistics (Mathematics)
                Subject: Statistical Methods
                Subject: Analysis of Variance
                Subject: Medicine and Health Sciences
                Subject: Pharmaceutics
                Subject: Drug Therapy
                Subject: Neurological Drug Therapy
                Subject: Anti-Addiction Drug Therapy
                Subject: Nicotine Replacement Therapy
                Subject: Medicine and Health Sciences
                Subject: Geriatrics
                Subject: Balance and Falls
                Subject: Research and Analysis Methods
                Subject: Research Design
                Subject: Experimental Design
                Subject: Medicine and Health Sciences
                Subject: Pulmonology
                Subject: Asthma
                Custom metadata
                Data Availability All relevant data are within the paper and its Supporting Information files.

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