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      Thyroid Cancer Following Childhood Low-Dose Radiation Exposure: A Pooled Analysis of Nine Cohorts

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          Abstract

          Context:

          The increased use of diagnostic and therapeutic procedures that involve radiation raises concerns about radiation effects, particularly in children and the radiosensitive thyroid gland.

          Objectives:

          Evaluation of relative risk (RR) trends for thyroid radiation doses <0.2 gray (Gy); evidence of a threshold dose; and possible modifiers of the dose-response, e.g., sex, age at exposure, time since exposure.

          Design and Setting:

          Pooled data from nine cohort studies of childhood external radiation exposure and thyroid cancer with individualized dose estimates, ≥1000 irradiated subjects or ≥10 thyroid cancer cases, with data limited to individuals receiving doses <0.2 Gy.

          Participants:

          Cohorts included the following: childhood cancer survivors (n = 2); children treated for benign diseases (n = 6); and children who survived the atomic bombings in Japan (n = 1). There were 252 cases and 2,588,559 person-years in irradiated individuals and 142 cases and 1,865,957 person-years in nonirradiated individuals.

          Intervention:

          There were no interventions.

          Main Outcome Measure:

          Incident thyroid cancers.

          Results:

          For both <0.2 and <0.1 Gy, RRs increased with thyroid dose ( P < 0.01), without significant departure from linearity ( P = 0.77 and P = 0.66, respectively). Estimates of threshold dose ranged from 0.0 to 0.03 Gy, with an upper 95% confidence bound of 0.04 Gy. The increasing dose–response trend persisted >45 years after exposure, was greater at younger age at exposure and younger attained age, and was similar by sex and number of treatments.

          Conclusions:

          Our analyses reaffirmed linearity of the dose response as the most plausible relationship for “as low as reasonably achievable” assessments for pediatric low-dose radiation-associated thyroid cancer risk.

          Abstract

          A pooling of nine cohort studies of childhood external radiation exposure revealed a linear increase in risk of thyroid cancer and reaffirmed the “as low as reasonably achievable” principal for pediatric low dose radiation.

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          Most cited references47

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          Quantifying heterogeneity in a meta-analysis.

          The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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            Meta-analysis in clinical trials.

            This paper examines eight published reviews each reporting results from several related trials. Each review pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These reviews lack consistent assessment of homogeneity of treatment effect before pooling. We discuss a random effects approach to combining evidence from a series of experiments comparing two treatments. This approach incorporates the heterogeneity of effects in the analysis of the overall treatment efficacy. The model can be extended to include relevant covariates which would reduce the heterogeneity and allow for more specific therapeutic recommendations. We suggest a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.
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              Meta-analysis in clinical trials revisited.

              In this paper, we revisit a 1986 article we published in this Journal, Meta-Analysis in Clinical Trials, where we introduced a random-effects model to summarize the evidence about treatment efficacy from a number of related clinical trials. Because of its simplicity and ease of implementation, our approach has been widely used (with more than 12,000 citations to date) and the "DerSimonian and Laird method" is now often referred to as the 'standard approach' or a 'popular' method for meta-analysis in medical and clinical research. The method is especially useful for providing an overall effect estimate and for characterizing the heterogeneity of effects across a series of studies. Here, we review the background that led to the original 1986 article, briefly describe the random-effects approach for meta-analysis, explore its use in various settings and trends over time and recommend a refinement to the method using a robust variance estimator for testing overall effect. We conclude with a discussion of repurposing the method for Big Data meta-analysis and Genome Wide Association Studies for studying the importance of genetic variants in complex diseases.
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                Author and article information

                Journal
                J Clin Endocrinol Metab
                J. Clin. Endocrinol. Metab
                jcem
                jcem
                The Journal of Clinical Endocrinology and Metabolism
                Endocrine Society (Washington, DC )
                0021-972X
                1945-7197
                01 July 2017
                08 March 2017
                08 March 2017
                : 102
                : 7
                : 2575-2583
                Affiliations
                [1 ]Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892
                [2 ]University of Rochester School of Medicine and Dentistry, Department of Public Health Sciences, Rochester, New York 14642
                [3 ]Radiation Effects Research Foundation, Hiroshima 732-0815, Japan
                [4 ]Department of Oncology and Radiation Physics and the Oncological Centre, Sahlgrenska University Hospital, S-413-45 Goteborg, Sweden
                [5 ]University of Illinois College of Medicine, Section of Endocrinology, Diabetes, and Metabolism, Chicago, Illinois 60612
                [6 ]Centre for Childhood Cancer Survivor Studies, Department of Public Health and Epidemiology, University of Birmingham, Birmingham B15 2TT, United Kingdom
                [7 ]Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, Tennessee 38105-3678
                [8 ]Department of Medical Physics, Radiumhemmet, Karolinska University Hospital and Karolinska Institute, SE-171 76 Stockholm, Sweden
                [9 ]Oncology, Department of Radiation Sciences, Umeå University, 901 87 Umeå, Sweden
                [10 ]Cancer Epidemiology Research Unit, National Institute for Health and Medical Research–Institut Gustave Roussy, 94 805 Villejuif, France
                [11 ]Cancer and Radiation Epidemiology Unit, Gertner Institute, Chaim Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Tel Hashomer, 52621 Israel
                [12 ]Institute for Radiation Protection and Dosimetry, Brazilian Nuclear Energy Commission, 22783-127 Rio de Janeiro, Brazil
                Author notes
                Address all correspondence and requests for reprints to: Jay H. Lubin, PhD, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Room 7E116, MSC 9780, Bethesda, Maryland 20892. E-mail: lubinj@ 123456mail.nih.gov .
                Article
                jcem_163529
                10.1210/jc.2016-3529
                5505197
                28323979
                73400ee1-1bb3-47a5-a61c-933a45d497d2
                History
                : 25 October 2016
                : 02 March 2017
                Page count
                Figures: 3, Tables: 2, Equations: 2, References: 50, Pages: 9
                Categories
                Clinical Research Articles
                Thyroid

                Endocrinology & Diabetes
                Endocrinology & Diabetes

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