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      Association of Vitamin D Receptor BsmI Gene Polymorphism with Risk of Tuberculosis: A Meta-Analysis of 15 Studies

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          Genetic variations in vitamin D receptor (VDR) may contribute to tuberculosis (TB) risk. Many studies have investigated the association between VDR BsmI gene polymorphism and TB risk, but yielded inconclusive results.

          Methodology/Principal Findings

          We performed a comprehensive meta-analysis of 15 publications with a total of 2309 cases and 3568 controls. We assessed the strength of the association between VDR BsmI gene polymorphism and TB risk and performed sub-group analyses by ethnicity, sample size and Hardy–Weinberg equilibrium (HWE). We found a statistically significant correlation between VDR BsmI gene polymorphism and decreased TB risk in four comparison models: allele model (b vs. B: OR = 0.78, 95% CI = 0.67, 0.89; P heterogeneity = 0.004), homozygote model (bb vs. BB: OR = 0.61, 95% CI = 0.43, 0.87; P heterogeneity = 0.001), recessive model (bb vs. Bb+BB: OR = 0.70, 95% CI = 0.56, 0.88; P heterogeneity = 0.005) and dominant model (bb+Bb vs. BB: OR = 0.77, 95% CI = 0.61, 0.97; P heterogeneity = 0.010), especially in studies based on Asian population. Sub-group analyses also revealed that there was a statistically decreased TB risk in “small” studies (<500 participants) and studies with P HWE>0.5. Meta-regression and stratification analysis both showed that the ethnicity and sample size contributed to heterogeneity.


          This meta-analysis suggests that VDR BsmI gene polymorphism is associated with a significant decreased TB risk, especially in Asian population.

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          Most cited references 40

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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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            Operating characteristics of a rank correlation test for publication bias.

            An adjusted rank correlation test is proposed as a technique for identifying publication bias in a meta-analysis, and its operating characteristics are evaluated via simulations. The test statistic is a direct statistical analogue of the popular "funnel-graph." The number of component studies in the meta-analysis, the nature of the selection mechanism, the range of variances of the effect size estimates, and the true underlying effect size are all observed to be influential in determining the power of the test. The test is fairly powerful for large meta-analyses with 75 component studies, but has only moderate power for meta-analyses with 25 component studies. However, in many of the configurations in which there is low power, there is also relatively little bias in the summary effect size estimate. Nonetheless, the test must be interpreted with caution in small meta-analyses. In particular, bias cannot be ruled out if the test is not significant. The proposed technique has potential utility as an exploratory tool for meta-analysts, as a formal procedure to complement the funnel-graph.
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              Statistical aspects of the analysis of data from retrospective studies of disease.

               N Mantel,  W Haenszel (1959)

                Author and article information

                [1 ]Department of Pharmacy, Changzhou Third People’s Hospital, Changzhou, China
                [2 ]College of Pharmacy, Soochow University, Suzhou, China
                [3 ]The Fourth Clinical College of Nanjing Medical University, Nanjing, China
                [4 ]Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital Cancer Institute of Jiangsu Province, Nanjing, China
                [5 ]Department of Bio-statistics, Georgia Health Science University, Augusta, Georgia, United States of America
                [6 ]Department of Pharmacy, Changzhou First People’s Hospital, Changzhou, China
                [7 ]Department of Pulmonary Tuberculosis, Changzhou Third People’s Hospital, Changzhou, China
                Fundacion Huesped, Argentina
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: YJW XY LX FLT. Performed the experiments: YJW XY XXW YZY ZXZ SMZ LX FLT. Analyzed the data: YJW XY XXW. Contributed reagents/materials/analysis tools: YJW XY XXW MTQ. Wrote the paper: YJW XY XXW. Access to full-text article: YJW XY.

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                25 June 2013
                : 8
                : 6
                23825591 3692555 PONE-D-13-12940 10.1371/journal.pone.0066944

                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.

                Pages: 8
                The authors have no support or funding to report.
                Research Article
                Population Genetics
                Genetic Polymorphism
                Population Biology
                Population Genetics
                Genetic Polymorphism
                Clinical Research Design
                Genetic Epidemiology
                Infectious Disease Epidemiology
                Infectious Diseases
                Bacterial Diseases
                Non-Clinical Medicine
                Health Care Policy
                Health Risk Analysis



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