covar_normal

Simulate from MVN with compound symmetry variance structure and
mean zero. The result is returned as a list where the ith element is the
column vector with n observations from the ith coordinate of the MVN.

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 

covar_normal function

<dl><dt>n</dt>
<dd>Number of samples</dd>
<dt>normal.cor</dt>
<dd>Correlation parameter (n x r) or (1 x r) matrix</dd>
<dt>normal.var</dt>
<dd>marginal variance (can be specified as a p-dim. vector or
a nxp matrix)</dd>
<dt>names</dt>
<dd>Column name of the column vector (default "z")</dd>
<dt>type</dt>
<dd>of correlation matrix structure (cs: compound-symmetry /
exchangable, ar: autoregressive, un: unstructured, to: toeplitz). The
dimension of <code>normal.cor</code> must match, i.e., for a Toeplitz correlation
matrix r = p-1, and for a cs and ar r=1.</dd>
<dt>...</dt>
<dd>Additional arguments passed to lower level functions</dd></dl>

Arguments

Simulate from multivariate normal distribution — covar_normal

<dl>

<dt>n</dt>
<dd>Number of samples</dd>


<dt>normal.cor</dt>
<dd>Correlation parameter (n x r) or (1 x r) matrix</dd>


<dt>normal.var</dt>
<dd>marginal variance (can be specified as a p-dim. vector or
a nxp matrix)</dd>


<dt>names</dt>
<dd>Column name of the column vector (default "z")</dd>


<dt>type</dt>
<dd>of correlation matrix structure (cs: compound-symmetry /
exchangable, ar: autoregressive, un: unstructured, to: toeplitz). The
dimension of <code>normal.cor</code> must match, i.e., for a Toeplitz correlation
matrix r = p-1, and for a cs and ar r=1.</dd>


<dt>...</dt>
<dd>Additional arguments passed to lower level functions</dd>

</dl>

covar_normal: Simulate from multivariate normal distribution

Description

Usage

Value

Arguments

See Also