metasens: Statistical Methods for Sensitivity Analysis 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>; - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) <DOI:10.1097/XEB.0000000000000141>.

Package details

AuthorGuido Schwarzer [cre, aut] (<https://orcid.org/0000-0001-6214-9087>), James R. Carpenter [aut] (<https://orcid.org/0000-0003-3890-6206>), Gerta Rücker [aut] (<https://orcid.org/0000-0002-2192-2560>)
MaintainerGuido Schwarzer <sc@imbi.uni-freiburg.de>
LicenseGPL (>= 2)
Version0.6-0
URL https://github.com/guido-s/metasens http://meta-analysis-with-r.org
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("metasens")

Try the metasens package in your browser

Any scripts or data that you put into this service are public.

metasens documentation built on Jan. 16, 2021, 5:38 p.m.