impute_outcome

Draws a random sample from a multivariate normal distribution.

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 

impute_outcome function

<dl><dt>conditional_parameters</dt>
<dd>a list with elements <code>mu</code> and <code>sigma</code> which
contain the mean vector and covariance matrix to sample from.</dd>
<dt>n_imputations</dt>
<dd>numeric representing the number of random samples from the multivariate
normal distribution to be performed. Default is <code>1</code>.</dd>
<dt>condmean</dt>
<dd>should conditional mean imputation be performed (as opposed to random
sampling)</dd></dl>

Arguments

Sample outcome value — impute_outcome

<dl>

<dt>conditional_parameters</dt>
<dd>a list with elements <code>mu</code> and <code>sigma</code> which
contain the mean vector and covariance matrix to sample from.</dd>


<dt>n_imputations</dt>
<dd>numeric representing the number of random samples from the multivariate
normal distribution to be performed. Default is <code>1</code>.</dd>


<dt>condmean</dt>
<dd>should conditional mean imputation be performed (as opposed to random
sampling)</dd>

</dl>

impute_outcome: Sample outcome value

Description

Usage

Arguments