f_sim

simulation binary data two outcomes

internal

Endpoint selection and sample size reassessment for multiple binary endpoints based on blinded and/or unblinded data. Trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation between endpoints. The implemented design is proposed in Bofill Roig, M., Gómez Melis, G., Posch, M., and Koenig, F. (2022). <doi:10.48550/arXiv.2206.09639>.

Marta Roig

eselect

Adaptive Clinical Trial Designs with Endpoint Selection and
Sample Size Reassessment

Marta Bofill Roig

Guadalupe Gomez Melis

Franz Koenig

Martin Posch

f_sim function

<dl><dt>samplesize</dt>
<dd>sample size simulated sample</dd>
<dt>p_e1</dt>
<dd>numeric parameter, probability of occurrence E1</dd>
<dt>p_e2</dt>
<dd>numeric parameter, probability of occurrence E2</dd>
<dt>p_ce</dt>
<dd>numeric parameter, probability of occurrence composite endpoint</dd></dl>

Arguments

Simulation 2x2 table binary endpoints — f_sim

<dl>

<dt>samplesize</dt>
<dd>sample size simulated sample</dd>


<dt>p_e1</dt>
<dd>numeric parameter, probability of occurrence E1</dd>


<dt>p_e2</dt>
<dd>numeric parameter, probability of occurrence E2</dd>


<dt>p_ce</dt>
<dd>numeric parameter, probability of occurrence composite endpoint</dd>

</dl>

f_sim: Simulation 2x2 table binary endpoints

Description

Usage

Value

Arguments