metamisc-package: Meta-Analysis of Diagnosis and Prognosis Research Studies
In metamisc: Meta-Analysis of Diagnosis and Prognosis Research Studies
Description
Details
Author(s)
References
See Also
Description
Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.
Details
The following functionality is currently implemented: univariate meta-analysis of summary data (uvmeta), bivariate meta-analysis of correlated outcomes (riley), meta-analysis of prediction model performance (valmeta), evaluation of funnel plot asymmetry (fat).
The metamisc package also provides a comprehensive framework for developing prediction models when patient-level data from multiple studies or settings are available (metapred).
Author(s)
Thomas Debray <thomas.debray@gmail.com>, Valentijn de Jong <Valentijn.M.T.de.Jong@gmail.com>
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;40(15):3533–59.
Debray TPA, Moons KGM, Ahmed I, Koffijberg H, Riley RD. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med. 2013;32(18):3158–80.
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.
Debray TPA, Damen JAAG, Snell KIE, Ensor J, Hooft L, Reitsma JB, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ. 2017;356:i6460.
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.
Riley RD, Moons K, Snell KIE, Ensor J, Hooft L, Altman D, et al. A guide to systematic review and meta-analysis of prognostic factor studies. BMJ. 2019;364:k4597.
Riley RD, Tierney JF, Stewart LA. Individual participant data meta-analysis: a handbook for healthcare research. Hoboken, NJ: Wiley; 2021. ISBN: 978-1-119-33372-2.
Schmid CH, Stijnen T, White IR. Handbook of meta-analysis. First edition. Boca Raton: Taylor and Francis; 2020. ISBN: 978-1-315-11940-3.
Steyerberg EW, Nieboer D, Debray TPA, Van Houwelingen JC. Assessment of heterogeneity in an individual participant data meta-analysis of prediction models: An overview and illustration. Stat Med. 2019;38(22):4290–309.
See Also
fat, metapred, riley, uvmeta, valmeta
metamisc documentation built on Oct. 13, 2021, 3 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details Author(s) References See Also
Description
Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.
Details
The following functionality is currently implemented: univariate meta-analysis of summary data (uvmeta), bivariate meta-analysis of correlated outcomes (riley), meta-analysis of prediction model performance (valmeta), evaluation of funnel plot asymmetry (fat).
The metamisc package also provides a comprehensive framework for developing prediction models when patient-level data from multiple studies or settings are available (metapred).
Author(s)
Thomas Debray <thomas.debray@gmail.com>, Valentijn de Jong <Valentijn.M.T.de.Jong@gmail.com>
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;40(15):3533–59.
Debray TPA, Moons KGM, Ahmed I, Koffijberg H, Riley RD. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med. 2013;32(18):3158–80.
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.
Debray TPA, Damen JAAG, Snell KIE, Ensor J, Hooft L, Reitsma JB, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ. 2017;356:i6460.
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.
Riley RD, Moons K, Snell KIE, Ensor J, Hooft L, Altman D, et al. A guide to systematic review and meta-analysis of prognostic factor studies. BMJ. 2019;364:k4597.
Riley RD, Tierney JF, Stewart LA. Individual participant data meta-analysis: a handbook for healthcare research. Hoboken, NJ: Wiley; 2021. ISBN: 978-1-119-33372-2.
Schmid CH, Stijnen T, White IR. Handbook of meta-analysis. First edition. Boca Raton: Taylor and Francis; 2020. ISBN: 978-1-315-11940-3.
Steyerberg EW, Nieboer D, Debray TPA, Van Houwelingen JC. Assessment of heterogeneity in an individual participant data meta-analysis of prediction models: An overview and illustration. Stat Med. 2019;38(22):4290–309.
See Also
fat, metapred, riley, uvmeta, valmeta
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.