fisher.bound

For a given control group response rate and sample sizes, this function
finds the smallest number of responders in the experimental arm (<code>rt</code>) that
achieves statistical significance based on a one-sided Fisher's exact test.

Implements Bayesian hybrid designs that incorporate historical
control data into a current clinical trial. The package uses a dynamic
power prior method to determine the degree of borrowing from the historical
data, creating a 'hybrid' control arm. This approach is primarily
designed for studies with a binary primary endpoint, such as the overall
response rate (ORR). Functions are provided for design calibration,
sample size calculation, power evaluation, and final analysis.
Additionally, it includes functions adapted from the 'SAMprior' package
(v1.1.1) by Yang et al. (2023) <https://academic.oup.com/biometrics/article/79/4/2857/7587575> to support the
Self-Adapting Mixture (SAM) prior framework for comparison.

Zhaohua Lu

BayesianHybridDesign

Bayesian Hybrid Design and Analysis

Philip He

fisher.bound function

<dl><dt>pc</dt>
<dd>A scalar numeric. The response rate for the control arm.</dd>
<dt>nc</dt>
<dd>A scalar integer. The number of subjects in the control arm.</dd>
<dt>nt</dt>
<dd>A scalar integer. The number of subjects in the experimental arm.</dd>
<dt>alpha</dt>
<dd>A scalar numeric. The one-sided p-value threshold for
statistical significance. Default is 0.1.</dd></dl>

Arguments

Calculate Rejection Boundary for Fisher's Exact Test — fisher.bound

<dl>

<dt>pc</dt>
<dd>A scalar numeric. The response rate for the control arm.</dd>


<dt>nc</dt>
<dd>A scalar integer. The number of subjects in the control arm.</dd>


<dt>nt</dt>
<dd>A scalar integer. The number of subjects in the experimental arm.</dd>


<dt>alpha</dt>
<dd>A scalar numeric. The one-sided p-value threshold for
statistical significance. Default is 0.1.</dd>

</dl>

fisher.bound: Calculate Rejection Boundary for Fisher's Exact Test

Description

Usage

Value

Arguments

Examples