sizeWR

Sample size calculation for the win ratio test (with WR = 1 null hypothesis)

Simulate and analyze hierarchical composite endpoints. Win odds is the main analysis method, but other win statistics (win ratio, net benefit) are also implemented, provided there is no censoring. See Gasparyan SB et al (2023) "Hierarchical Composite Endpoints in COVID-19: The DARE-19 Trial." Case Studies in Innovative Clinical Trials, 95-148. Chapman; Hall/CRC. <doi:10.1201/9781003288640-7>.

Samvel B. Gasparyan

Design and Analysis of Hierarchical Composite Endpoints

sizeWR function

<dl><dt>WR</dt>
<dd>a numeric vector of win odds values.</dd>
<dt>power</dt>
<dd>the given power. A numeric vector of length 1.</dd>
<dt>WO</dt>
<dd>win odds. Should be specified only if <code>Pties</code> is not specified. A numeric vector of length 1.</dd>
<dt>Pties</dt>
<dd>probability of ties. A numeric vector of length 1.</dd>
<dt>k</dt>
<dd>proportion of active group in the overall sample size. Default is 0.5 (balanced randomization). A numeric vector of length 1.</dd>
<dt>alpha</dt>
<dd>the significance level for the 2-sided test. Default is 0.05. A numeric vector of length 1.</dd></dl>

Arguments

Sample size calculation for the win ratio test (with WR = 1 null hypothesis) — sizeWR

<dl>

<dt>WR</dt>
<dd>a numeric vector of win odds values.</dd>


<dt>power</dt>
<dd>the given power. A numeric vector of length 1.</dd>


<dt>WO</dt>
<dd>win odds. Should be specified only if <code>Pties</code> is not specified. A numeric vector of length 1.</dd>


<dt>Pties</dt>
<dd>probability of ties. A numeric vector of length 1.</dd>


<dt>k</dt>
<dd>proportion of active group in the overall sample size. Default is 0.5 (balanced randomization). A numeric vector of length 1.</dd>


<dt>alpha</dt>
<dd>the significance level for the 2-sided test. Default is 0.05. A numeric vector of length 1.</dd>

</dl>

sizeWR: Sample size calculation for the win ratio test (with WR = 1 null hypothesis)

Description

Usage

Value

Arguments

References

See Also

Examples