JSD (Fujikawa et al., 2020) Method for Bayesian Basket Trial

Provides tools for Bayesian basket trial design and analysis using a novel three-component local power prior framework with global borrowing control, pairwise similarity assessment and a borrowing threshold. Supports simulation-based evaluation of operating characteristics and comparison with other methods. Applicable to both equal and unequal sample size settings in early-phase oncology trials. For more details see Zhou et al. (2023) <doi:10.48550/arXiv.2312.15352>.

Haiming Zhou

BasketTrial

Bayesian Basket Trial Design and Analysis

Philip He

JSD function

<dl><dt>nDat</dt>
<dd>a vector of length B for the sample size in each basket.</dd>
<dt>yDat</dt>
<dd>a vector of length B for the number of responses in each basket.</dd>
<dt>be.a0</dt>
<dd>a vector of length B for beta prior parameter a0 in each basket.</dd>
<dt>be.b0</dt>
<dd>a vector of length B for beta prior parameter b0 in each basket.</dd>
<dt>epsilon</dt>
<dd>the global control parameter in the JSD model.</dd>
<dt>tau</dt>
<dd>the threshold parameter in the JSD model.</dd></dl>

Arguments

JSD (Fujikawa et al., 2020) Method for Bayesian Basket Trial — JSD

<dl>

<dt>nDat</dt>
<dd>a vector of length B for the sample size in each basket.</dd>


<dt>yDat</dt>
<dd>a vector of length B for the number of responses in each basket.</dd>


<dt>be.a0</dt>
<dd>a vector of length B for beta prior parameter a0 in each basket.</dd>


<dt>be.b0</dt>
<dd>a vector of length B for beta prior parameter b0 in each basket.</dd>


<dt>epsilon</dt>
<dd>the global control parameter in the JSD model.</dd>


<dt>tau</dt>
<dd>the threshold parameter in the JSD model.</dd>

</dl>

JSD: JSD (Fujikawa et al., 2020) Method for Bayesian Basket Trial

Description

Usage

Value

Arguments

References

Examples