remove_if_all_missing

This function takes a <code>data.frame</code> with variables <code>visit</code>, <code>outcome</code> &amp; <code>subjid</code>.
It then removes all rows for a given <code>subjid</code> if they don't have any non-missing
values for <code>outcome</code>.

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 

remove_if_all_missing function

<dl><dt>dat</dt>
<dd>a <code>data.frame</code></dd></dl>

Arguments

Remove subjects from dataset if they have no observed values — remove_if_all_missing

<dl>

<dt>dat</dt>
<dd>a <code>data.frame</code></dd>

</dl>

remove_if_all_missing: Remove subjects from dataset if they have no observed values

Description

Usage

Arguments