as_analysis

Creates an analysis object ensuring that all components are
correctly defined.

Implements standard and reference based multiple imputation methods for continuous
longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular,
this package supports deterministic conditional mean imputation and jackknifing as described
in Wolbers et al. (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described
in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum
likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.

Craig Gower-Page

rbmi

Reference Based Multiple Imputation

Alessandro Noci

Marcel Wolbers

Isaac Gravestock

F. Hoffmann-La Roche AG 

as_analysis function

<dl><dt>results</dt>
<dd>A list of lists contain the analysis results for each imputation
See <code>analyse()</code> for details on what this object should look like.</dd>
<dt>method</dt>
<dd>The method object as specified in <code>draws()</code>.</dd>
<dt>delta</dt>
<dd>The delta dataset used. See <code>analyse()</code> for details on how this
should be specified.</dd>
<dt>fun</dt>
<dd>The analysis function that was used.</dd>
<dt>fun_name</dt>
<dd>The character name of the analysis function (used for printing)
purposes.</dd></dl>

Arguments

Construct an analysis object — as_analysis

<dl>

<dt>results</dt>
<dd>A list of lists contain the analysis results for each imputation
See <code>analyse()</code> for details on what this object should look like.</dd>


<dt>method</dt>
<dd>The method object as specified in <code>draws()</code>.</dd>


<dt>delta</dt>
<dd>The delta dataset used. See <code>analyse()</code> for details on how this
should be specified.</dd>


<dt>fun</dt>
<dd>The analysis function that was used.</dd>


<dt>fun_name</dt>
<dd>The character name of the analysis function (used for printing)
purposes.</dd>

</dl>

as_analysis: Construct an `analysis` object

Description

Usage

Arguments