print.mb.rank

Prints a summary of rankings for each parameter

Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) that allows inclusion of multiple
time-points from studies. Repeated measures over time are accounted for within studies by applying different time-course functions,
following the method of Pedder et al. (2019) <doi:10.1002/jrsm.1351>.
The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple
treatment comparisons. Several general time-course functions are provided; others may be added
by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and 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.

Hugo Pedder

MBNMAtime

Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models

print.mb.rank function

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

Arguments

Prints a summary of rankings for each parameter — print.mb.rank

<dl>

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


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

</dl>

print.mb.rank: Prints a summary of rankings for each parameter

Description

Usage

Value

Arguments