outcome_shared

Monte Carlo simulation framework for different randomized clinical
trial designs with a special emphasis on estimators based on covariate
adjustment. The package implements regression-based covariate adjustment
(Rosenblum & van der Laan (2010) <doi:10.2202/1557-4679.1138>) and a
one-step estimator (Van Lancker et al (2024)
<doi:10.48550/arXiv.2404.11150>) for trials with continuous, binary and
count outcomes. The estimation of the minimum sample-size required to reach
a specified statistical power for a given estimator uses bisection to find
an initial rough estimate, followed by stochastic approximation
(Robbins-Monro (1951) <doi:10.1214/aoms/1177729586>) to improve the
estimate, and finally, a grid search to refine the estimate in the
neighborhood of the current best solution.

Benedikt Sommer

carts

Simulation-Based Assessment of Covariate Adjustment in
Randomized Trials

Klaus K. Holst

Foroogh Shamsi

Novo Nordisk A/S 

outcome_shared function

<dl><dt>data</dt>
<dd>(data.table) Covariate data, usually the output of the covariate
model of a Trial object.</dd>
<dt>par</dt>
<dd>(numeric) Regression coefficients (default zero). Can be given as
a named list corresponding to the column names of <code>model.matrix</code></dd>
<dt>outcome.name</dt>
<dd>Name of outcome variable ("y")</dd>
<dt>remove</dt>
<dd>Variables that will be removed from input <code>data</code> (if formula is
not specified).</dd>
<dt>mean</dt>
<dd>(formula, function) Either a formula specifying the design from
'data' or a function that maps <code>data</code> to the conditional mean value on the
link scale (see examples). If NULL all main-effects of the covariates will
be used, except columns that are defined via the <code>remove</code> argument.</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code>mean</code> function (see examples)</dd></dl>

Arguments

Outcome model — outcome_shared

<dl>

<dt>data</dt>
<dd>(data.table) Covariate data, usually the output of the covariate
model of a Trial object.</dd>


<dt>par</dt>
<dd>(numeric) Regression coefficients (default zero). Can be given as
a named list corresponding to the column names of <code>model.matrix</code></dd>


<dt>outcome.name</dt>
<dd>Name of outcome variable ("y")</dd>


<dt>remove</dt>
<dd>Variables that will be removed from input <code>data</code> (if formula is
not specified).</dd>


<dt>mean</dt>
<dd>(formula, function) Either a formula specifying the design from
'data' or a function that maps <code>data</code> to the conditional mean value on the
link scale (see examples). If NULL all main-effects of the covariates will
be used, except columns that are defined via the <code>remove</code> argument.</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code>mean</code> function (see examples)</dd>

</dl>

outcome_shared: Outcome model

Description

Value

Arguments