validate.is_mar

Checks that the longitudinal data for a patient is divided in MAR
followed by non-MAR data; a non-MAR observation followed by a MAR
observation is not allowed.

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 

validate.is_mar function

<dl><dt>x</dt>
<dd>Object of class <code>is_mar</code>. Logical vector indicating whether observations are MAR.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Validate is_mar for a given subject — validate.is_mar

<dl>

<dt>x</dt>
<dd>Object of class <code>is_mar</code>. Logical vector indicating whether observations are MAR.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

validate.is_mar: Validate `is_mar` for a given subject

Description

Usage

Value

Arguments