print.mbnma.rank

Prints summary information about an mbnma.rank object

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.mbnma.rank function

<dl><dt>x</dt>
<dd>An object of class <code>"mbnma.rank"</code> generated by <code>rank.mbnma()</code></dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods</dd></dl>

Arguments

Prints summary information about an mbnma.rank object — print.mbnma.rank

<dl>

<dt>x</dt>
<dd>An object of class <code>"mbnma.rank"</code> generated by <code>rank.mbnma()</code></dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods</dd>

</dl>

print.mbnma.rank: Prints summary information about an mbnma.rank object

Description

Usage

Arguments