Statistical Methods for Sensitivity Analysis in Meta-Analysis R package metasens version 1.5-3 CRAN Task View: ClinicalTrials (https://CRAN.R-project.org/view=ClinicalTrials) CRAN Task View: MetaAnalysis (https://CRAN.R-project.org/view=MetaAnalysis) CRAN Task View: MissingData (https://CRAN.R-project.org/view=MissingData) 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>; - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) <DOI:10.1097/XEB.0000000000000141>. Software Depends: meta (>= 8.0-1) Guido Schwarzer <guido.schwarzer@uniklinik-freiburg.de> Comprehensive R Archive Network (CRAN) Guido Schwarzer [cre, aut] (ORCID: <https://orcid.org/0000-0001-6214-9087>), James R. Carpenter [aut] (ORCID: <https://orcid.org/0000-0003-3890-6206>), Gerta Rücker [aut] (ORCID: <https://orcid.org/0000-0002-2192-2560>) GPL (>= 2) 2025-05-02 application/tgz https://CRAN.R-project.org/package=metasens doi:10.32614/CRAN.package.metasens