as_mmrm_df

Converts a design matrix + key variables into a common format
In particular this function does the following:<ul>
<li>Renames all covariates as <code>V1</code>, <code>V2</code>, etc to avoid issues of special characters in variable names</li>
<li>Ensures all key variables are of the right type</li>
<li>Inserts the outcome, visit and subjid variables into the <code>data.frame</code>
naming them as <code>outcome</code>, <code>visit</code> and <code>subjid</code></li>
<li>If provided will also insert the group variable into the <code>data.frame</code> named as <code>group</code></li>
</ul>

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>.

Isaac Gravestock

rbmi

Reference Based Multiple Imputation

Craig Gower-Page

Alessandro Noci

Marcel Wolbers

Daniel Sabanes Bove

F. Hoffmann-La Roche AG 

as_mmrm_df function

<dl><dt>designmat</dt>
<dd>a <code>data.frame</code> or <code>matrix</code> containing the covariates to use in the MMRM model.
Dummy variables must already be expanded out, i.e. via <code><a href="/link/stats%3A%3Amodel.matrix()?package=rbmi&version=1.4.1" data-mini-rdoc="rbmi::stats::model.matrix()">stats::model.matrix()</a></code>. Cannot contain
any missing values</dd>
<dt>outcome</dt>
<dd>a numeric vector. The outcome value to be regressed on in the MMRM model.</dd>
<dt>visit</dt>
<dd>a character / factor vector. Indicates which visit the outcome value occurred on.</dd>
<dt>subjid</dt>
<dd>a character / factor vector. The subject identifier used to link separate visits
that belong to the same subject.</dd>
<dt>group</dt>
<dd>a character / factor vector. Indicates which treatment group the patient belongs to.</dd></dl>

Arguments

Creates a "MMRM" ready dataset — as_mmrm_df

<dl>

<dt>designmat</dt>
<dd>a <code>data.frame</code> or <code>matrix</code> containing the covariates to use in the MMRM model.
Dummy variables must already be expanded out, i.e. via <code><a href='https://rdrr.io/r/stats/model.matrix.html'>stats::model.matrix()</a></code>. Cannot contain
any missing values</dd>


<dt>outcome</dt>
<dd>a numeric vector. The outcome value to be regressed on in the MMRM model.</dd>


<dt>visit</dt>
<dd>a character / factor vector. Indicates which visit the outcome value occurred on.</dd>


<dt>subjid</dt>
<dd>a character / factor vector. The subject identifier used to link separate visits
that belong to the same subject.</dd>


<dt>group</dt>
<dd>a character / factor vector. Indicates which treatment group the patient belongs to.</dd>

</dl>

as_mmrm_df: Creates a "MMRM" ready dataset

Description

Usage

Arguments