forest

Generate a forest plot by specifying the various effect sizes, confidence intervals and summary estimate.

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>.

Thomas Debray

metamisc

Meta-Analysis of Diagnosis and Prognosis Research Studies

Valentijn de Jong

forest function

<dl><dt>...</dt>
<dd>Additional arguments, which are currently ignored.</dd></dl>

Arguments

Thomas Debray &lt;thomas.debray@gmail.com&gt;
Valentijn de Jong &lt;Valentijn.M.T.de.Jong@gmail.com&gt;

Author

Forest plot — forest

<dl>

<dt>...</dt>
<dd>Additional arguments, which are currently ignored.</dd>

</dl>

forest: Forest plot

Description

Usage

Arguments

Author

Details