power_best_binomial

Computes the exact probability of correctly identifying the best group
when the outcome follows a binomial distribution. It assumes that <code>p1</code>
is the probability of success in the best group, and that the success
probability in all other groups is lower by a fixed difference <code>dif</code>.

Functions for sample size estimation and simulation in clinical
trials. Includes methods for selecting the best group using the
Indifference-zone approach, as well as designs for non-inferiority,
equivalence, and negative binomial models. For the sample size calculation
for non-inferiority of vaccines, the approach is based on Fleming, Powers,
and Huang (2021) <doi:10.1177/1740774520988244>. The Indifference-zone
approach is based on Sobel and Huyett (1957) <doi:10.1002/j.1538-7305.1957.tb02411.x> and
Bechhofer, Santner, and Goldsman (1995, ISBN:978-0-471-57427-9).

Aponte John

ssutil

Sample Size Calculation Tools

John J. Aponte

Chris Gast

power_best_binomial function

<dl><dt>p1</dt>
<dd>Numeric. Probability of success in the best group (must be in [0, 1]).</dd>
<dt>dif</dt>
<dd>Numeric. Difference in success probability between the best group and the next best (must be &gt; 0).</dd>
<dt>ngroups</dt>
<dd>Integer. Number of groups (must be greater than 1).</dd>
<dt>npergroup</dt>
<dd>Integer. Number of subjects per group (must be positive).</dd></dl>

Arguments

Power to Correctly Select the Best Group in a Binomial Test — power_best_binomial

<dl>

<dt>p1</dt>
<dd>Numeric. Probability of success in the best group (must be in [0, 1]).</dd>


<dt>dif</dt>
<dd>Numeric. Difference in success probability between the best group and the next best (must be &gt; 0).</dd>


<dt>ngroups</dt>
<dd>Integer. Number of groups (must be greater than 1).</dd>


<dt>npergroup</dt>
<dd>Integer. Number of subjects per group (must be positive).</dd>

</dl>

power_best_binomial: Power to Correctly Select the Best Group in a Binomial Test

Description

Usage

Value

Arguments

Details

References

Examples