Title: Bayesian Models for Cost-Effectiveness Analysis in the Presence

Title: Clinical Trial Calculation Based on BCHM Design
Description: Users can estimate the treatment effect for multiple subgroups basket trials based on the Bayesian Cluster Hierarchical Model (BCHM). In this model, a Bayesian non-parametric method is applied to dynamically calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the Bayesian non-parametric clustering and the similarities between subgroups. To use this package, 'JAGS' software and 'rjags' package are required, and users need to pre-install them.
Imports: stats, cluster, coda, clValid, knitr, crayon, plyr

Imports: stats, cluster, coda, knitr, crayon, plyr

Maintainer: J. Jack Lee <jjlee@mdanderson.org>

Description: Functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals.

Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.

Title: A Bayesian Semiparametric Factor Analysis Model for Subtype
Author: Jiehuan Sun [aut, cre], Joshua L. Warren [aut], and Hongyu Zhao [aut]
Description: Gene expression profiles are commonly utilized to infer disease
subtypes and many clustering methods can be adopted for this task.
However, existing clustering methods may not perform well when
genes are highly correlated and many uninformative genes are included
for clustering. To deal with these challenges, we develop a novel
clustering method in the Bayesian setting. This method, called BCSub,
adopts an innovative semiparametric Bayesian factor analysis model
to reduce the dimension of the data to a few factor scores for
clustering. Specifically, the factor scores are assumed to follow
the Dirichlet process mixture model in order to induce clustering.
Packaged: 2016-10-29 03:10:28 UTC; JiehuanSun