Frequentist.Analysis

Performs a frequentist analysis of a two-arm study using current data only.

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

Frequentist.Analysis function

<dl><dt>Yt</dt>
<dd>A scalar. The number of responses in the experimental arm.</dd>
<dt>nt</dt>
<dd>A scalar. The number of subjects in the experimental arm.</dd>
<dt>Yc</dt>
<dd>A scalar. The number of responses in the control arm.</dd>
<dt>nc</dt>
<dd>A scalar. The number of subjects in the control arm.</dd>
<dt>conflev</dt>
<dd>A scalar. The desired confidence level for the confidence intervals.</dd></dl>

Arguments

Statistical Analysis Using Frequentist Method — Frequentist.Analysis

<dl>

<dt>Yt</dt>
<dd>A scalar. The number of responses in the experimental arm.</dd>


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


<dt>Yc</dt>
<dd>A scalar. The number of responses in the control arm.</dd>


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


<dt>conflev</dt>
<dd>A scalar. The desired confidence level for the confidence intervals.</dd>

</dl>

Frequentist.Analysis: Statistical Analysis Using Frequentist Method

Description

Usage

Value

Arguments

Examples