setup_bhm {basksim}

Setup BHM Design Object

Description

Setup BHM Design Object

Usage

setup_bhm(k, p0, p_target, mu_mean = NULL, mu_sd = 100)

Arguments

k

The number of baskets.

p0

A common probability under the null hypothesis.

p_target

The response rate of interest. See details.

mu_mean

Mean of the normal prior distribution for the mean of the thetas. See details.

mu_sd

Standard deviation of the normal prior distribution for the mean of the thetas.

Details

The class bhm implements the Bayesian Hierarchical Model proposed by Berry et al. (2013). Methods for this class are mostly wrappers for functions from the package bhmbasket.

In the BHM the thetas of all baskets are modeled, where theta_i = logit(p_i) - logit(p_target). These thetas are assumed to come from a normal distribution with mean mu_mean and standard deviation mu_sd. If mu_mean = NULL then mu_mean is determined as logit(p0) - logit(p_target), hence the mean of the normal distribution corresponds to the null hypothesis.

Value

An S3 object of class bhm

References

Berry, S. M., Broglio, K. R., Groshen, S., & Berry, D. A. (2013). Bayesian hierarchical modeling of patient subpopulations: efficient designs of phase II oncology clinical trials. Clinical Trials, 10(5), 720-734.

Examples

design_bhm <- setup_bhm(k = 3, p0 = 0.2, p_target = 0.5)