About Anaconda Help Download Anaconda

r / packages / r-metasens

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>.

Click on a badge to see how to embed it in your web page
badge
https://anaconda.org/r/r-metasens/badges/version.svg
badge
https://anaconda.org/r/r-metasens/badges/latest_release_date.svg
badge
https://anaconda.org/r/r-metasens/badges/latest_release_relative_date.svg
badge
https://anaconda.org/r/r-metasens/badges/platforms.svg
badge
https://anaconda.org/r/r-metasens/badges/license.svg
badge
https://anaconda.org/r/r-metasens/badges/downloads.svg

© 2024 Anaconda, Inc. All Rights Reserved. (v4.0.6) Legal | Privacy Policy