get_pattern_groups_unique

Takes a dataset of pattern information and creates a summary dataset of it
with just 1 row per pattern

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_unique function

<dl><dt>patterns</dt>
<dd>A <code>data.frame</code> with the columns <code>pgroup</code>, <code>pattern</code> and <code>group</code></dd></dl>

Arguments

Get Pattern Summary — get_pattern_groups_unique

<dl>

<dt>patterns</dt>
<dd>A <code>data.frame</code> with the columns <code>pgroup</code>, <code>pattern</code> and <code>group</code></dd>

</dl>

get_pattern_groups_unique: Get Pattern Summary

Description

Usage

Arguments

Details