<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Victoria N Nyaga</style></author><author><style face="normal" font="default" size="100%">M. Arbyn</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Methods for meta-analysis and meta-regression of binomial data: concepts and tutorial with Stata command metapreg</style></title><secondary-title><style face="normal" font="default" size="100%">Archives of Public Health</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Binomal</style></keyword><keyword><style  face="normal" font="default" size="100%">logistic regression</style></keyword><keyword><style  face="normal" font="default" size="100%">meta-analyses</style></keyword><keyword><style  face="normal" font="default" size="100%">Meta-regressions</style></keyword><keyword><style  face="normal" font="default" size="100%">Network Meta-Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Stata</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">29/01/2024</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">82</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;strong style=&quot;color:#69aa41;&quot;&gt;Background&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Despite the widespread interest in meta-analysis of proportions, its rationale, certain theoretical and methodological concepts are poorly understood. The generalized linear models framework is well-established and provides a natural and optimal model for meta-analysis, network meta-analysis, and meta-regression of proportions. Nonetheless, generic methods for meta-analysis of proportions based on the approximation to the normal distribution continue to dominate.&lt;/p&gt;

&lt;p&gt;&lt;strong style=&quot;color:#69aa41;&quot;&gt;Methods&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;We developed&amp;nbsp;metapreg, a tool with advanced statistical procedures to perform a meta-analysis, network meta-analysis, and meta-regression of binomial proportions in Stata using binomial, logistic and logistic-normal models. First, we explain the rationale and concepts essential in understanding statistical methods for meta-analysis of binomial proportions and describe the models implemented in&amp;nbsp;metapreg. We then describe and demonstrate the models in&amp;nbsp;metapreg&amp;nbsp;using data from seven published meta-analyses. We also conducted a simulation study to compare the performance of&amp;nbsp;metapreg&amp;nbsp;estimators with the existing estimators of the population-averaged proportion in&amp;nbsp;metaprop&amp;nbsp;and&amp;nbsp;metan&amp;nbsp;under a broad range of conditions including, high over-dispersion and small meta-analysis.&lt;/p&gt;

&lt;p&gt;&lt;strong style=&quot;color:#69aa41;&quot;&gt;Conclusion&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;metapreg&amp;nbsp;is a flexible, robust and user-friendly tool employing a rigorous approach to evidence synthesis of binomial data that makes the most efficient use of all available data and does not require ad-hoc continuity correction or data imputation. We expect its use to yield higher-quality meta-analysis of binomial proportions.&lt;/p&gt;
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