print.relative.array

Print posterior medians (95% credible intervals) for table of relative effects/mean
differences between treatments/classes

Fits Bayesian dose-response model-based network meta-analysis (MBNMA)
that incorporate multiple doses within an agent by modelling different dose-response functions, as
described by Mawdsley et al. (2016) <doi:10.1002/psp4.12091>.
By modelling dose-response relationships this can connect networks of evidence that might
otherwise be disconnected, and can improve precision on treatment estimates. Several common
dose-response functions are provided; others may be added by the user. Various characteristics
and assumptions can be flexibly added to the models, such as shared class effects. The consistency
of direct and indirect evidence in the network can be assessed using unrelated mean effects models
and/or by node-splitting at the treatment level.

Hugo Pedder

MBNMAdose

Dose-Response MBNMA Models

Adil Karim

print.relative.array function

<dl><dt>x</dt>
<dd>An object of class <code>"relative.array"</code> generated by <code>get.relative()</code></dd>
<dt>digits</dt>
<dd>An integer indicating the number of significant digits to be used.</dd>
<dt>...</dt>
<dd>further arguments passed to <code><a href="/link/knitr%3A%3Akable?package=MBNMAdose&version=0.4.3" data-mini-rdoc="MBNMAdose::knitr::kable">knitr::kable</a></code></dd></dl>

Arguments

Print posterior medians (95% credible intervals) for table of relative effects/mean
differences between treatments/classes — print.relative.array

<dl>

<dt>x</dt>
<dd>An object of class <code>"relative.array"</code> generated by <code>get.relative()</code></dd>


<dt>digits</dt>
<dd>An integer indicating the number of significant digits to be used.</dd>


<dt>...</dt>
<dd>further arguments passed to <code><a href='https://rdrr.io/pkg/knitr/man/kable.html'>knitr::kable</a></code></dd>

</dl>

print.relative.array: Print posterior medians (95% credible intervals) for table of relative effects/mean differences between treatments/classes

Description

Usage

Arguments