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      Body mass index and survival in women with breast cancer—systematic literature review and meta-analysis of 82 follow-up studies

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          The current systematic literature review and meta-analysis extends and confirms the associations of obesity with an unfavourable overall and breast cancer survival in pre and postmenopausal breast cancer, regardless of when BMI is ascertained. Increased risks of mortality in underweight and overweight women and J-shape associations with total mortality were also observed. The recommendation of maintaining a healthy body weight throughout life is important as obesity is a pandemic health concern.



          Positive association between obesity and survival after breast cancer was demonstrated in previous meta-analyses of published data, but only the results for the comparison of obese versus non-obese was summarised.


          We systematically searched in MEDLINE and EMBASE for follow-up studies of breast cancer survivors with body mass index (BMI) before and after diagnosis, and total and cause-specific mortality until June 2013, as part of the World Cancer Research Fund Continuous Update Project. Random-effects meta-analyses were conducted to explore the magnitude and the shape of the associations.


          Eighty-two studies, including 213 075 breast cancer survivors with 41 477 deaths (23 182 from breast cancer) were identified. For BMI before diagnosis, compared with normal weight women, the summary relative risks (RRs) of total mortality were 1.41 [95% confidence interval (CI) 1.29–1.53] for obese (BMI >30.0), 1.07 (95 CI 1.02–1.12) for overweight (BMI 25.0–<30.0) and 1.10 (95% CI 0.92–1.31) for underweight (BMI <18.5) women. For obese women, the summary RRs were 1.75 (95% CI 1.26–2.41) for pre-menopausal and 1.34 (95% CI 1.18–1.53) for post-menopausal breast cancer. For each 5 kg/m 2 increment of BMI before, <12 months after, and ≥12 months after diagnosis, increased risks of 17%, 11%, and 8% for total mortality, and 18%, 14%, and 29% for breast cancer mortality were observed, respectively.


          Obesity is associated with poorer overall and breast cancer survival in pre- and post-menopausal breast cancer, regardless of when BMI is ascertained. Being overweight is also related to a higher risk of mortality. Randomised clinical trials are needed to test interventions for weight loss and maintenance on survival in women with breast cancer.

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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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            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


                Author and article information

                Ann Oncol
                Ann. Oncol
                Annals of Oncology
                Oxford University Press
                October 2014
                27 April 2014
                27 April 2014
                : 25
                : 10
                : 1901-1914
                [1 ]Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London , London, UK
                [2 ]Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology , Trondheim, Norway
                [3 ]Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey , New Jersey, USA
                [4 ]Division of Biostatistics, Centre for Epidemiology and Biostatistics, University of Leeds , Leeds, UK
                [5 ]Division of Public Health Sciences, Fred Hutchinson Cancer Research Center , Washington, USA
                [6 ]Department of Oncology, Oslo University Hospital , Oslo
                [7 ]Faculty of Health Sciences, Department of Community Medicine, University of Tromso , Tromso, Norway
                [8 ]School of Mathematics and Statistics, University of Newcastle , Newcastle upon Tyne, UK
                Author notes
                [* ] Correspondence to: Doris S. M. Chan, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK. Tel: +44-0-20-759-48590; Fax: +44-0-20-759-43193; E-mail: d.chan@
                © The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@



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