prepare_prior_params

Based on the <code>covariance</code> and <code>prior_cov</code> choices, this function prepares the prior parameters
for the covariance model.

internal

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>.

Craig Gower-Page

rbmi

Reference Based Multiple Imputation

Isaac Gravestock

Alessandro Noci

Marcel Wolbers

Daniel Sabanes Bove

F. Hoffmann-La Roche AG 

prepare_prior_params function

<dl><dt>stan_data</dt>
<dd>A list containing the Stan data, to which the prior parameters will be added.</dd>
<dt>covariance</dt>
<dd>A character string indicating the covariance structure (e.g., "us", "ar1").</dd>
<dt>prior_cov</dt>
<dd>A character string indicating the prior covariance type (e.g., "default", "lkj").</dd>
<dt>mmrm_initial</dt>
<dd>A list containing the initial MMRM fit results, which includes
the relevant frequentist estimates.</dd>
<dt>same_cov</dt>
<dd>A logical indicating whether to use the same covariance structure for all groups.</dd></dl>

Arguments

Prepare Prior Parameters for Covariance Model Prior Distribution — prepare_prior_params

<dl>

<dt>stan_data</dt>
<dd>A list containing the Stan data, to which the prior parameters will be added.</dd>


<dt>covariance</dt>
<dd>A character string indicating the covariance structure (e.g., "us", "ar1").</dd>


<dt>prior_cov</dt>
<dd>A character string indicating the prior covariance type (e.g., "default", "lkj").</dd>


<dt>mmrm_initial</dt>
<dd>A list containing the initial MMRM fit results, which includes
the relevant frequentist estimates.</dd>


<dt>same_cov</dt>
<dd>A logical indicating whether to use the same covariance structure for all groups.</dd>

</dl>

prepare_prior_params: Prepare Prior Parameters for Covariance Model Prior Distribution

Description

Usage

Value

Arguments