metasens: Advanced Statistical Methods to Model and Adjust for Bias in Meta-Analysis

The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 5 'Small-Study Effects in Meta-Analysis': - Copas selection model described in Copas & Shi (2001) <doi:10.1177/096228020101000402>; - limit meta-analysis by Rücker et al. (2011) <doi:10.1093/biostatistics/kxq046>; - upper bound for outcome reporting bias by Copas & Jackson (2004) <doi:10.1111/j.0006-341X.2004.00161.x>; - imputation methods for missing binary data by Gamble & Hollis (2005) <doi:10.1016/j.jclinepi.2004.09.013> and Higgins et al. (2008) <doi:10.1177/1740774508091600>.

Version:	0.4-1
Depends:	meta (≥ 4.9-5)
Published:	2020-07-02
Author:	Guido Schwarzer [cre, aut], James R. Carpenter [aut], Gerta Rücker [aut]
Maintainer:	Guido Schwarzer <sc at imbi.uni-freiburg.de>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/guido-s/metasens http://meta-analysis-with-r.org
NeedsCompilation:	no
Materials:	NEWS
In views:	ClinicalTrials, MetaAnalysis, MissingData
CRAN checks:	metasens results

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