Statistical Methods for Sensitivity Analysis in Meta-Analysis
Copas selection model analysis
Doi plot for Asymmetry
Forest plot for orbbound object (bound for outcome reporting bias)
Funnel plot for limit meta-analysis
LFK Index Test for Asymmetry
Limit meta-analysis
Imputation methods for missing binary data
metasens: Brief overview of methods and general hints
Sensitivity Analysis for Outcome Reporting Bias (ORB)
Display results of Copas selection modelling
Print results of Copas selection model
Print results for limit meta-analysis
Print method for objects of class orbbound
Print detailed results of Copas selection model
Print detailed results for limit meta-analysis
Summary method for Copas selection model
Summary method for limit 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>.