This package fits biomarker threshold regression models for predictive and prognostic biomarker effects with binary data and survival data with an unknown biomarker cutoff point. Multivariable models can also be fitted for adjusted biomarker effect. Tools such as Probability index are included to measure treatment effect, biomarker effect or treatment-biomarker interaction.
"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.
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. (2017). A Bayesian method for risk window estimatin with application to HPV vaccine trial. Computational Statistics and Data Analysis. 112, page 53-62.
Jiang, S., Chen, B. E. and Tu, D.(2016). Inference on treatment-covariate interaction based on a nonparametric m easure of treatment effects and censored survival data. Statistics in Medicine. 35, 2715-2725.
coxph,
glm,
survival
# NOT RUN {
# fit = bhm(y~biomarker+treatment)
# }
Run the code above in your browser using DataLab