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[Application of Meta-regression and subgroup analyses of heterogeneity disposal in Meta-analysis].

Xiuquan Shi, Zengzhen Wang

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Abstract

To explore the role and application of Meta-regression and subgroup analyses to recognize and control the heterogeneity in Meta-analysis, Meta-regression models were established by secondary data to screen the factors resulting heterogeneity, and subgroup analyses were used to compare the change of heterogeneity before and after. The heterogeneity was found in the Meta-analysis (Q = 44.71, df = 27, P = 0.017). Sample size and region were selected (P = 0.012 and P = 0.091, respectively) by Meta-regression from many possible factors such as sample size, year, region and case/ control ratio. The Q values were lowered from 44.71 to 32.11 after subgroup analyses. Thus, Meta-regression method was convenient and reliable to screen the affected factors of heterogeneity, and subgroup analyses based on the hypothesis that could significantly lower the heterogeneity. It was recommended to a combined use when an obvious heterogeneity existed but was in need to get an overall result in Meta-analysis. We could correctly judge and lower the heterogeneity to increase the robustness and rationality of results from Meta-analysis.

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What this paper is about

To explore the role and application of Meta-regression and subgroup analyses to recognize and control the heterogeneity in Meta-analysis, Meta-regression models were established by secondary data to screen the factors resulting heterogeneity, and subgroup analyses were used to compare the change of heterogeneity before and after. The heterogeneity was found in the Meta-analysis (Q = 44.71, df = 27, P = 0.017). Sample size and region were selected (P = 0.012 and P = 0.091, respectively) by Meta-regression from many possible factors such as sample size, year, region and case/ control ratio. The Q values were lowered from 44.71 to 32.11 after subgroup analyses. Thus, Meta-regression method was convenient and reliable to screen the affected factors of heterogeneity, and subgroup analyses based on the hypothesis that could significantly lower the heterogeneity. It was recommended to a combined use when an obvious heterogeneity existed but was in need to get an overall result in Meta-analysis. We could correctly judge and lower the heterogeneity to increase the robustness and rationality of results from Meta-analysis.

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Available abstract

To explore the role and application of Meta-regression and subgroup analyses to recognize and control the heterogeneity in Meta-analysis, Meta-regression models were established by secondary data to screen the factors resulting heterogeneity, and subgroup analyses were used to compare the change of heterogeneity before and after. The heterogeneity was found in the Meta-analysis (Q = 44.71, df = 27, P = 0.017). Sample size and region were selected (P = 0.012 and P = 0.091, respectively) by Meta-regression from many possible factors such as sample size, year, region and case/ control ratio. The Q values were lowered from 44.71 to 32.11 after subgroup analyses. Thus, Meta-regression method was convenient and reliable to screen the affected factors of heterogeneity, and subgroup analyses based on the hypothesis that could significantly lower the heterogeneity. It was recommended to a combined use when an obvious heterogeneity existed but was in need to get an overall result in Meta-analysis. We could correctly judge and lower the heterogeneity to increase the robustness and rationality of results from Meta-analysis.

Key concepts: Meta-analysis, Meta-regression, Subgroup analysis, Publication bias, Sample size determination, Statistics, Regression analysis, Study heterogeneity

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