write.cor

This uses a Wishart prior as default for modelling the correlation

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

write.cor function

<dl><dt>model</dt>
<dd>A character object of JAGS MBNMA model code</dd>
<dt>fun</dt>
<dd>An object of class <code>"timefun"</code> generated (see Details) using any of
<code>tloglin()</code>, <code>tpoly()</code>, <code>titp()</code>, <code>temax()</code>, <code>tfpoly()</code>, <code>tspline()</code> or <code>tuser()</code></dd>
<dt>omega</dt>
<dd>DEPRECATED IN VERSION 0.2.3 ONWARDS (~uniform(-1,1) now used for correlation between parameters
rather than a Wishart prior).
A scale matrix for the inverse-Wishart prior for the covariance matrix used
to model the correlation between time-course parameters (see Details for time-course functions). <code>omega</code> must
be a symmetric positive definite matrix with dimensions equal to the number of time-course parameters modelled using
relative effects (<code>pool="rel"</code>). If left as <code>NULL</code> (the default) a diagonal matrix with elements equal to 1
is used.</dd>
<dt>class.effect</dt>
<dd>A list of named strings that determines which time-course
parameters to model with a class effect and what that effect should be
(<code>"common"</code> or <code>"random"</code>). For example: <code>list(emax="common", et50="random")</code>.</dd></dl>

Arguments

Adds correlation between time-course relative effects — write.cor

<dl>

<dt>model</dt>
<dd>A character object of JAGS MBNMA model code</dd>


<dt>fun</dt>
<dd>An object of class <code>"timefun"</code> generated (see Details) using any of
<code>tloglin()</code>, <code>tpoly()</code>, <code>titp()</code>, <code>temax()</code>, <code>tfpoly()</code>, <code>tspline()</code> or <code>tuser()</code></dd>


<dt>omega</dt>
<dd>DEPRECATED IN VERSION 0.2.3 ONWARDS (~uniform(-1,1) now used for correlation between parameters
rather than a Wishart prior).
A scale matrix for the inverse-Wishart prior for the covariance matrix used
to model the correlation between time-course parameters (see Details for time-course functions). <code>omega</code> must
be a symmetric positive definite matrix with dimensions equal to the number of time-course parameters modelled using
relative effects (<code>pool="rel"</code>). If left as <code>NULL</code> (the default) a diagonal matrix with elements equal to 1
is used.</dd>


<dt>class.effect</dt>
<dd>A list of named strings that determines which time-course
parameters to model with a class effect and what that effect should be
(<code>"common"</code> or <code>"random"</code>). For example: <code>list(emax="common", et50="random")</code>.</dd>

</dl>

write.cor: Adds correlation between time-course relative effects

Description

Usage

Arguments