metamisc

This is the official repository of the R package metamisc, which was developed to facilitate meta-analysis of diagnosis and prognosis research studies. The package includes functions for the following tasks:

To develop and validate multivariable prediction models from datasets with clustering (de Jong et al., 2021)
To summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019)
To evaluate funnel plot asymmetry (Debray et al., 2018)

Installation

The metamisc package can be installed from CRAN as follows:

install.packages("metamisc")

You can install the development version of metamisc from GitHub with:

# install.packages("devtools")
# devtools::install_github("smartdata-analysis-and-statistics/metamisc")
install.packages("metamisc", repos = c('https://smartdata-analysis-and-statistics.r-universe.dev', 'https://cloud.r-project.org'))

JASP

A visual interface to the software has been implemented by JASP https://jasp-stats.org/

Funding

The development of this R package has been funded by the following organisations:

The Netherlands Organisation for Health Research and Development (grant 91617050).
The European Union’s Horizon 2020 research and innovation programme under ReCoDID grant agreement No 825746.
Smart Data Analysis and Statistics B.V., a limited liability corporation registered at the Netherlands Chamber of Commerce under number 863595327.

References

de Jong VMT, Moons KGM, Eijkemans MJC, Riley RD, Debray TPA. Developing more generalizable prediction models from pooled studies and large clustered data sets. Stat Med. 2021 May 5;40(15):3533–59.

Debray TPA, Moons KGM, Riley RD. Detecting small-study effects and funnel plot asymmetry in meta-analysis of survival data: a comparison of new and existing tests. Res Syn Meth. 2018;9(1):41–50.

Debray TPA, Damen JAAG, Riley R, Snell KIE, Reitsma JB, Hooft L, et al. A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes. Stat Methods Med Res. 2019 Sep;28(9):2768–86.