ancova_core

Internal function that provides the common functionality for the (multi-arm) ANCOVA
analysis. This function handles input validation, visit processing, delegates the
actual single-visit analysis to <code>ancova_single()</code>, and assembles the per-parameter
metadata.

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>.

Lukas A. Widmer

rbmi

Reference Based Multiple Imputation

Craig Gower-Page

Isaac Gravestock

Alessandro Noci

Marcel Wolbers

Daniel Sabanes Bove

F. Hoffmann-La Roche AG 

ancova_core function

<dl><dt>data</dt>
<dd>A <code>data.frame</code> containing the data to be used in the model.</dd>
<dt>vars</dt>
<dd>A <code>vars</code> object as generated by <code>set_vars()</code>. Only the <code>group</code>,
<code>visit</code>, <code>outcome</code> and <code>covariates</code> elements are required. See details.</dd>
<dt>visits</dt>
<dd>An optional character vector specifying which visits to
fit the ANCOVA model at. If <code>NULL</code>, a separate ANCOVA model will be fit to the
outcomes for each visit (as determined by <code>unique(data[[vars$visit]])</code>).
See details.</dd>
<dt>weights</dt>
<dd>Character, either <code>"counterfactual"</code> (default), <code>"equal"</code>,
<code>"proportional_em"</code> or <code>"proportional"</code>.
Specifies the weighting strategy to be used when calculating the lsmeans.
See the weighting section for more details.</dd></dl>

Arguments

Core ANCOVA Implementation — ancova_core

<dl>

<dt>data</dt>
<dd>A <code>data.frame</code> containing the data to be used in the model.</dd>


<dt>vars</dt>
<dd>A <code>vars</code> object as generated by <code>set_vars()</code>. Only the <code>group</code>,
<code>visit</code>, <code>outcome</code> and <code>covariates</code> elements are required. See details.</dd>


<dt>visits</dt>
<dd>An optional character vector specifying which visits to
fit the ANCOVA model at. If <code>NULL</code>, a separate ANCOVA model will be fit to the
outcomes for each visit (as determined by <code>unique(data[[vars$visit]])</code>).
See details.</dd>


<dt>weights</dt>
<dd>Character, either <code>"counterfactual"</code> (default), <code>"equal"</code>,
<code>"proportional_em"</code> or <code>"proportional"</code>.
Specifies the weighting strategy to be used when calculating the lsmeans.
See the weighting section for more details.</dd>

</dl>

ancova_core: Core ANCOVA Implementation

Description

Usage

Arguments