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.
- Version1.5-2
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release02/28/2023
Documentation
Team
Guido Schwarzer
MaintainerShow author detailsGerta Rücker
James R. Carpenter
Insights
Last 30 days
This package has been downloaded 1,400 times in the last 30 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 78 times.
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Last 365 days
This package has been downloaded 18,330 times in the last 365 days. That's enough downloads to make it mildly famous in niche technical communities. A badge of honor! The day with the most downloads was Nov 08, 2024 with 191 downloads.
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Dependencies
- Depends1 package