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.
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.