metamisc-package: Meta-Analysis of Diagnosis and Prognosis Research Studies
In metamisc: Meta-Analysis of Diagnosis and Prognosis Research Studies
metamisc-package R Documentation
Meta-Analysis of Diagnosis and Prognosis Research Studies
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 Sept. 25, 2022, 5:05 p.m.
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| metamisc-package | R Documentation |
Meta-Analysis of Diagnosis and Prognosis Research Studies
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
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