derive_covar_distribution

Derive covariate distribution (for outcome regression) for
integers (Poisson), numeric (Gaussian) and binary (Binomial) data. Raise an
error for other data types.

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 

derive_covar_distribution function

<dl><dt>covar</dt>
<dd>Vector with covariates</dd></dl>

Arguments

Author

Derive covariate distribution from covariate data type — derive_covar_distribution

<dl>

<dt>covar</dt>
<dd>Vector with covariates</dd>

</dl>

derive_covar_distribution: Derive covariate distribution from covariate data type

Description

Usage

Value

Arguments

Author