exactci

Calculates the two-sided Clopper-Pearson exact confidence interval for a
binomial proportion.

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

exactci function

<dl><dt>r</dt>
<dd>A scalar integer. The number of successes or responses.</dd>
<dt>n</dt>
<dd>A scalar integer. The total number of trials or subjects.</dd>
<dt>conflev</dt>
<dd>A scalar numeric. The desired confidence level (e.g., 0.95
for a 95% CI).</dd></dl>

Arguments

Clopper-Pearson Exact Confidence Interval for a Binomial Proportion — exactci

<dl>

<dt>r</dt>
<dd>A scalar integer. The number of successes or responses.</dd>


<dt>n</dt>
<dd>A scalar integer. The total number of trials or subjects.</dd>


<dt>conflev</dt>
<dd>A scalar numeric. The desired confidence level (e.g., 0.95
for a 95% CI).</dd>

</dl>

exactci: Clopper-Pearson Exact Confidence Interval for a Binomial Proportion

Description

Usage

Value

Arguments

Examples