power_best_normal

Estimate the probability of correctly select the best group among
<code>ngroups</code> groups if the difference between the best group and the next best
is at least <code>dif</code>(the Indifferent-Zone), and the standard deviation is <code>sd</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_normal function

<dl><dt>dif</dt>
<dd>Numeric. Indifferent-zone. Minimum difference that is considered meaningful.</dd>
<dt>sd</dt>
<dd>Numeric. Common standard deviation of the response variable.</dd>
<dt>ngroups</dt>
<dd>Integer. Number of groups (treatments) being compared.</dd>
<dt>npergroup</dt>
<dd>Integer. Number in each group.</dd>
<dt>seed</dt>
<dd>Optional. Integer seed to use in the internal call to <code>multp()</code>.</dd></dl>

Arguments

Power calculation for the Indifferent-zone approach for normal outcomes — power_best_normal

<dl>

<dt>dif</dt>
<dd>Numeric. Indifferent-zone. Minimum difference that is considered meaningful.</dd>


<dt>sd</dt>
<dd>Numeric. Common standard deviation of the response variable.</dd>


<dt>ngroups</dt>
<dd>Integer. Number of groups (treatments) being compared.</dd>


<dt>npergroup</dt>
<dd>Integer. Number in each group.</dd>


<dt>seed</dt>
<dd>Optional. Integer seed to use in the internal call to <code>multp()</code>.</dd>

</dl>

power_best_normal: Power calculation for the Indifferent-zone approach for normal outcomes

Description

Usage

Value

Arguments

Examples