bhm (version 1.1)

bhm-package: Biomarker Threshold Models

Description

This package fits a threshold regression model for predictive and prognostic biomarker effect for binary data and survival data with an unknown biomarker cutoff point. Multivariable models can also be fitted for adjusted biomarker effect.

Details

"bhm" is a R package for Biomarker Threshold Models. Please use the following steps to install 'bhm' package:

1. First, you need to install the 'devtools' package. You can skip this step if you have 'devtools' installed in your R. Invoke R and then type

install.packages("devtools")

2. Load the devtools package.

library(devtools)

3. Install "bhm" package with R commond

install_github("statapps/bhm")

"bhm" uses different statistical methods to identify cut-point (thershold parameter) for the biomarker in either generalized linear models or Cox proportional hazards model.

References

Chen, B. E., Jiang, W. and Tu, D. (2014). A hierarchical Bayes model for biomarker subset effects in clinical trials. Computational Statistics and Data Analysis. vol 71, page 324-334.

Fang, T., Mackillop, W., Jiang, W., Hildesheim, A., Wacholder, S. and Chen, B. E. (2016). A Bayesian Model for Estimating a Risk Window: Methods and Application. Manuscript submitted to: Computational Statistics and Data Analysis.

See Also

coxph, glm, survival

Examples

# fit = bhm(y~biomarker+treatment)