est_phreg

Marginal Cox proportional hazards model for the treatment effect in
RCT

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 

est_phreg function

<dl><dt>response</dt>
<dd>Response variable (character or formula). Default:
"Surv(time, status)"</dd>
<dt>treatment</dt>
<dd>Treatment variable (character)</dd>
<dt>level</dt>
<dd>Confidence interval level</dd>
<dt>id</dt>
<dd>Optional subject id variable (character)</dd></dl>

Arguments

Author

Marginal Cox proportional hazards model for the treatment effect in
RCT — est_phreg

<dl>

<dt>response</dt>
<dd>Response variable (character or formula). Default:
"Surv(time, status)"</dd>


<dt>treatment</dt>
<dd>Treatment variable (character)</dd>


<dt>level</dt>
<dd>Confidence interval level</dd>


<dt>id</dt>
<dd>Optional subject id variable (character)</dd>

</dl>

est_phreg: Marginal Cox proportional hazards model for the treatment effect in RCT

Description

Usage

Value

Arguments

Author

See Also