Description

R package metasens provides advanced statistical methods to model and adjust bias in meta-analysis and supports Schwarzer et al. (2015), Chapter 5 "Small-Study Effects in Meta-Analysis" http://meta-analysis-with-r.org/.

Details

R package metasens is an add-on package for meta providing the following meta-analysis methods:

• Copas selection model (function copas) described in Copas & Shi (2001) and evaluated in Schwarzer et al., 2010);

• limit meta-analysis (limitmeta) by Rücker et al. (2011);

• upper bound for outcome reporting bias (orbbound) described in Copas & Jackson (2004);

• imputation methods for missing binary data (metamiss) described in Gamble & Hollis (2005) and Higgins et al. (2008).

Furthermore, functions and datasets from metasens are utilised in Schwarzer et al. (2015), Chapter 5 "Small-Study Effects in Meta-Analysis", http://meta-analysis-with-r.org/.

Type help(package = "metasens") for a listing of R functions available in metasens.

Type citation("metasens") on how to cite metasens in publications.

To report problems and bugs

• type bug.report(package = "metasens") if you do not use RStudio,

• send an email to Guido Schwarzer sc@imbi.uni-freiburg.de if you use RStudio.

The development version of metasens is available on GitHub https://github.com/guido-s/metasens.

Author(s)

Guido Schwarzer sc@imbi.uni-freiburg.de

References

Copas J, Jackson D (2004): A bound for publication bias based on the fraction of unpublished studies. Biometrics, 60, 146–53

Copas JB, Shi JQ (2001): A sensitivity analysis for publication bias in systematic reviews. Statistical Methods in Medical Research, 10, 251–65

Gamble C, Hollis S (2005): Uncertainty method improved on best–worst case analysis in a binary meta-analysis. Journal of Clinical Epidemiology, 58, 579–88

Higgins JPT, White IR, Wood AM (2008): Imputation methods for missing outcome data in meta-analysis of clinical trials. Clinical Trials, 5, 225–39

Rücker G, Schwarzer G, Carpenter JR, Binder H, Schumacher M (2011): Treatment-effect estimates adjusted for small-study effects via a limit meta-analysis. Biostatistics, 12, 122–42

Schwarzer G, Carpenter J, Rücker G (2010): Empirical evaluation suggests Copas selection model preferable to trim-and-fill method for selection bias in meta-analysis. Journal of Clinical Epidemiology, 63, 282–8

Schwarzer G, Carpenter JR, Rücker G (2015): Meta-Analysis with R (Use-R!). Springer International Publishing, Switzerland