summary.rankogram

print.summary.rankogram

Summary method for objects of class <code>rankogram</code> to print
list of studies in subnetworks.

A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis":
- frequentist network meta-analysis following Rücker (2012) <doi:10.1002/jrsm.1058>;
- additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <doi:10.1002/bimj.201800167>;
- network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>, or penalised logistic regression (Evrenoglou et al., 2022) <doi:10.1002/sim.9562>;
- rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>;
- ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>;
- split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>;
- league table with network meta-analysis results;
- 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>;
- net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>;
- measures characterizing the flow of evidence between two treatments by König et al. (2013) <doi:10.1002/sim.6001>;
- automated drawing of network graphs described in Rücker & Schwarzer (2016) <doi:10.1002/jrsm.1143>;
- partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>;
- contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>;
- network meta-regression with a single continuous or binary covariate;
- subgroup network meta-analysis.

Guido Schwarzer

netmeta

Network Meta-Analysis using Frequentist Methods

Gerta Rücker

Ulrike Krahn

Jochem König

Orestis Efthimiou

Annabel Davies

Theodoros Papakonstantinou

Theodoros Evrenoglou

Krzysztof Ciomek

Nana-Adjoa Kwarteng

summary.rankogram function

<dl><dt>object</dt>
<dd>An object of class <code>rankogram</code>.</dd>
<dt>...</dt>
<dd>Additional arguments (passed on to
<code>print.rankogram</code>.</dd>
<dt>x</dt>
<dd>An object of class <code>summary.rankogram</code>.</dd>
<dt>common</dt>
<dd>A logical indicating to print ranking probabilities
and SUCRAs for the common effects model.</dd>
<dt>random</dt>
<dd>A logical indicating to print ranking probabilities
and SUCRAs for the random effects model.</dd>
<dt>sort</dt>
<dd>A logical indicating whether treatments should be
sorted by decreasing SUCRAs.</dd>
<dt>nchar.trts</dt>
<dd>A numeric defining the minimum number of
characters used to create unique treatment names.</dd>
<dt>digits</dt>
<dd>Minimal number of significant digits for ranking probabilities
and SUCRAs, see <code><a href="/link/print.default?package=netmeta&version=3.2-0" data-mini-rdoc="netmeta::print.default">print.default</a></code>.</dd>
<dt>digits.mean</dt>
<dd>Minimal number of significant digits for mean ranks, see
<code><a href="/link/print.default?package=netmeta&version=3.2-0" data-mini-rdoc="netmeta::print.default">print.default</a></code>.</dd>
<dt>details.methods</dt>
<dd>A logical specifying whether details on statistical
methods should be printed.</dd>
<dt>legend</dt>
<dd>A logical indicating whether a legend should be
printed.</dd></dl>

Arguments

Guido Schwarzer <a href="/link/guido.schwarzer%40uniklinik-freiburg.de?package=netmeta&version=3.2-0" data-mini-rdoc="netmeta::guido.schwarzer@uniklinik-freiburg.de">guido.schwarzer@uniklinik-freiburg.de</a>

Author

Summary method for objects of class rankogram — summary.rankogram

<dl>

<dt>object</dt>
<dd>An object of class <code>rankogram</code>.</dd>


<dt>...</dt>
<dd>Additional arguments (passed on to
<code>print.rankogram</code>.</dd>


<dt>x</dt>
<dd>An object of class <code>summary.rankogram</code>.</dd>


<dt>common</dt>
<dd>A logical indicating to print ranking probabilities
and SUCRAs for the common effects model.</dd>


<dt>random</dt>
<dd>A logical indicating to print ranking probabilities
and SUCRAs for the random effects model.</dd>


<dt>sort</dt>
<dd>A logical indicating whether treatments should be
sorted by decreasing SUCRAs.</dd>


<dt>nchar.trts</dt>
<dd>A numeric defining the minimum number of
characters used to create unique treatment names.</dd>


<dt>digits</dt>
<dd>Minimal number of significant digits for ranking probabilities
and SUCRAs, see <code><a href='https://rdrr.io/r/base/print.default.html'>print.default</a></code>.</dd>


<dt>digits.mean</dt>
<dd>Minimal number of significant digits for mean ranks, see
<code><a href='https://rdrr.io/r/base/print.default.html'>print.default</a></code>.</dd>


<dt>details.methods</dt>
<dd>A logical specifying whether details on statistical
methods should be printed.</dd>


<dt>legend</dt>
<dd>A logical indicating whether a legend should be
printed.</dd>

</dl>

Guido Schwarzer <a href='mailto:guido.schwarzer@uniklinik-freiburg.de'>guido.schwarzer@uniklinik-freiburg.de</a>

summary.rankogram: Summary method for objects of class rankogram

Description

Usage

Arguments

Author

See Also

Examples