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      Mastering variation: variance components and personalised medicine

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          Abstract

          Various sources of variation in observed response in clinical trials and clinical practice are considered, and ways in which the corresponding components of variation might be estimated are discussed. Although the issues have been generally well‐covered in the statistical literature, they seem to be poorly understood in the medical literature and even the statistical literature occasionally shows some confusion. To increase understanding and communication, some simple graphical approaches to illustrating issues are proposed. It is also suggested that reducing variation in medical practice might make as big a contribution to improving health outcome as personalising its delivery according to the patient. It is concluded that the common belief that there is a strong personal element in response to treatment is not based on sound statistical evidence. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

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          Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.2 mutations.

          Ewan Pearson, Isabelle Flechtner, Pål R. Njølstad … (2006)
          Heterozygous activating mutations in KCNJ11, encoding the Kir6.2 subunit of the ATP-sensitive potassium (K(ATP)) channel, cause 30 to 58 percent of cases of diabetes diagnosed in patients under six months of age. Patients present with ketoacidosis or severe hyperglycemia and are treated with insulin. Diabetes results from impaired insulin secretion caused by a failure of the beta-cell K(ATP) channel to close in response to increased intracellular ATP. Sulfonylureas close the K(ATP) channel by an ATP-independent route. We assessed glycemic control in 49 consecutive patients with Kir6.2 mutations who received appropriate doses of sulfonylureas and, in smaller subgroups, investigated the insulin secretory responses to intravenous and oral glucose, a mixed meal, and glucagon. The response of mutant K(ATP) channels to the sulfonylurea tolbutamide was assayed in xenopus oocytes. A total of 44 patients (90 percent) successfully discontinued insulin after receiving sulfonylureas. The extent of the tolbutamide blockade of K(ATP) channels in vitro reflected the response seen in patients. Glycated hemoglobin levels improved in all patients who switched to sulfonylurea therapy (from 8.1 percent before treatment to 6.4 percent after 12 weeks of treatment, P<0.001). Improved glycemic control was sustained at one year. Sulfonylurea treatment increased insulin secretion, which was more highly stimulated by oral glucose or a mixed meal than by intravenous glucose. Exogenous glucagon increased insulin secretion only in the presence of sulfonylureas. Sulfonylurea therapy is safe in the short term for patients with diabetes caused by KCNJ11 mutations and is probably more effective than insulin therapy. This pharmacogenetic response to sulfonylureas may result from the closing of mutant K(ATP) channels, thereby increasing insulin secretion in response to incretins and glucose metabolism. (ClinicalTrials.gov number, NCT00334711 [ClinicalTrials.gov].). Copyright 2006 Massachusetts Medical Society.
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            Cluster trials in implementation research: estimation of intracluster correlation coefficients and sample size.

            J Grimshaw, K. Campbell, J. Mollison (2001)
            The cluster randomized trial with a concurrent economic evaluation is considered the gold standard evaluative design for the conduct of implementation research evaluating different strategies to promote the transfer of research findings into clinical practice. This has implications for the planning of such studies, as information is needed on the effects of clustering on both effectiveness and efficiency outcomes. This paper describes the design considerations specific to implementation research studies, focusing particularly on the estimation of sample size requirements and on the need for reliable information on intracluster correlation coefficients for both effectiveness and efficiency outcomes. Copyright 2001 John Wiley & Sons, Ltd.
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              Epidemiology, epigenetics and the 'Gloomy Prospect': embracing randomness in population health research and practice.

              George Davey Smith (2011)
              Epidemiologists aim to identify modifiable causes of disease, this often being a prerequisite for the application of epidemiological findings in public health programmes, health service planning and clinical medicine. Despite successes in identifying causes, it is often claimed that there are missing additional causes for even reasonably well-understood conditions such as lung cancer and coronary heart disease. Several lines of evidence suggest that largely chance events, from the biographical down to the sub-cellular, contribute an important stochastic element to disease risk that is not epidemiologically tractable at the individual level. Epigenetic influences provide a fashionable contemporary explanation for such seemingly random processes. Chance events-such as a particular lifelong smoker living unharmed to 100 years-are averaged out at the group level. As a consequence population-level differences (for example, secular trends or differences between administrative areas) can be entirely explicable by causal factors that appear to account for only a small proportion of individual-level risk. In public health terms, a modifiable cause of the large majority of cases of a disease may have been identified, with a wild goose chase continuing in an attempt to discipline the random nature of the world with respect to which particular individuals will succumb. The quest for personalized medicine is a contemporary manifestation of this dream. An evolutionary explanation of why randomness exists in the development of organisms has long been articulated, in terms of offering a survival advantage in changing environments. Further, the basic notion that what is near-random at one level may be almost entirely predictable at a higher level is an emergent property of many systems, from particle physics to the social sciences. These considerations suggest that epidemiological approaches will remain fruitful as we enter the decade of the epigenome.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Stat Med
                Journal ID (iso-abbrev): Stat Med
                Journal ID (doi): 10.1002/(ISSN)1097-0258
                Journal ID (publisher-id): SIM
                Title: Statistics in Medicine
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Print): 0277-6715
                ISSN (Electronic): 1097-0258
                Publication date (Electronic): 28 September 2015
                Publication date (Print): 30 March 2016
                Volume: 35
                Issue: 7 , Papers from the 35th Annual Conference of the International Society for Clinical Biostatistics ( doiID: 10.1002/sim.v35.7 )
                Pages: 966-977
                Affiliations
                [ 1 ] Competence Centre for Methodology and StatisticsLuxembourg Institute of Health L‐1445 StrassenLuxembourg
                Author notes
                [*] [* ] Correspondence to: Stephen Senn, Competence Centre for Methodology and Statistics, Luxembourg Institute of Health, L‐1445 Strassen, Luxembourg.

                E‐mail: stephen.senn@ 123456lih.lu

                Author information
                http://orcid.org/0000-0002-7558-8473
                Article
                Publisher ID: SIM6739 Other ID: SIM-14-0786.R1
                DOI: 10.1002/sim.6739
                PMC ID: 5054923
                PubMed ID: 26415869
                SO-VID: 3b3605e0-306c-4f3a-a224-b6c526802d19
                Copyright © © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
                License:

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                Date received : 31 October 2014
                Date revision received : 05 May 2015
                Date accepted : 31 August 2015
                Page count
                Pages: 12
                Funding
                Funded by: European Union FP7 programme
                Award ID: 602552
                Categories
                Subject: Special Issue Paper
                Subject: Special Issue Papers
                Custom metadata
                source-schema-version-number 2.0
                component-id sim6739
                cover-date 30 March 2016
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.4 mode:remove_FC converted:07.10.2016

                ScienceOpen disciplines: Biostatistics
                Keywords: personalised medicine,random effects,n‐of‐1 trials,cross‐over trials,components of variation
                Data availability:
                ScienceOpen disciplines: Biostatistics
                Keywords: personalised medicine, random effects, n‐of‐1 trials, cross‐over trials, components of variation

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