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Titlebook: Meta-Analysis with R; Guido Schwarzer,James R. Carpenter,Gerta Rücker Book 2015 Springer International Publishing Switzerland 2015 diagnos

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Fixed Effect and Random Effects Meta-AnalysisFor both models the inverse variance method is introduced for estimation. The pros and cons of these methods in various contexts have been debated at length in the literature [9, 28, 29, 41], without any conclusive resolution. Here, we briefly describe each model, and how it is estimated in the R package meta [33, 34].
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Meta-Analysis of Diagnostic Test Accuracy Studiese explain the issues raised by meta-analysis of diagnostic accuracy studies and how these may be addressed. Alongside the statistical models, we present the R package mada [5] written for fitting these models and graphing the results.
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https://doi.org/10.1007/978-3-319-21416-0diagnostic studies; meta-analysis; missing data; multivariate analysis; small study effects
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Use R!http://image.papertrans.cn/m/image/631185.jpg
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Heterogeneity and Meta-RegressionVarious aspects of statistical heterogeneity have been introduced briefly in previous chapters. We now review and develop these ideas and describe the connection between subgroup analysis and meta-regression.
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Network Meta-AnalysisNetwork meta-analysis (also known as multiple treatment comparison or mixed treatment comparison) seeks to combine information from all randomised comparisons among a set of treatments for a given medical condition. It is therefore a key tool for evidence-based medicine [24] and is currently a very active research topic [1, 21, 31, 32].
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978-3-319-21415-3Springer International Publishing Switzerland 2015
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Meta-Analysis with R978-3-319-21416-0Series ISSN 2197-5736 Series E-ISSN 2197-5744
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