get_pattern_groups

Takes a design matrix with multiple rows per subject and returns a dataset
with 1 row per subject with a new column <code>pgroup</code> indicating which group
the patient belongs to (based upon their missingness pattern and treatment group)

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 

get_pattern_groups function

<dl><dt>ddat</dt>
<dd>a <code>data.frame</code> with columns <code>subjid</code>, <code>visit</code>, <code>group</code>, <code>is_avail</code></dd></dl>

Arguments

Determine patients missingness group — get_pattern_groups

<dl>

<dt>ddat</dt>
<dd>a <code>data.frame</code> with columns <code>subjid</code>, <code>visit</code>, <code>group</code>, <code>is_avail</code></dd>

</dl>

get_pattern_groups: Determine patients missingness group

Description

Usage

Arguments

Details