metasens: Statistical Methods for Sensitivity Analysis in Meta-Analysis

Official Git repository of R package metasens

Description

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), Chapter 5 "Small-Study Effects in Meta-Analysis":

• Copas selection model (Copas and Shi, 2001; Schwarzer et al., 2010);
• limit meta-analysis (Rücker et al., 2011);
• upper bound for outcome reporting bias (Copas and Jackson, 2004);
• imputation methods for missing binary data (Gamble & Hollis, 2005; Higgins et al., 2008).
• LFK index test and Doi plot (Furuya-Kanamori et al., 2018).

Furthermore, R package metasens provides functions and datasets to support Schwarzer et al. (2015), Chapter 5 "Small-Study Effects in Meta-Analysis", https://link.springer.com/book/10.1007/978-3-319-21416-0 .

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Copas J, Jackson D (2004): A bound for publication bias based on the fraction of unpublished studies.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

Furuya-Kanamori L, Barendregt JJ, Doi S (2018): A new improved graphical and quantitative method for detecting bias in meta-analysis. International Journal of Evidence-Based Healthcare, 16, 195-203

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, 282-88

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

Installation

Current official release:

install.packages("metasens")

Current beta / GitHub release:

Installation using R package remotes:

install.packages("remotes")
remotes::install_github("guido-s/metasens")

Bug Reports:

You can report bugs on GitHub under Issues.

or using the R command

bug.report(package = "metasens")

(which is not supported in RStudio).