bhm-package: An R package for the biomarker threshold models

bhm-packageR Documentation

An R package for the biomarker threshold models

Description

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 (Chen et al, 2014)<DOI:10.1016/j.csda.2013.05.015>. Multivariable models can also be fitted for adjusted biomarker effect (Fang et al, 2017)<DOI:10.1016/j.csda.2017.02.011>. Tools such as Probability index are included to measure treatment effect, biomarker effect or treatment-biomarker interaction(Jiang et al, 2016)<DOI:10.1002/sim.6907>.

Details

"bhm" is a R package for Biomarker Threshold Models. Please use the following steps to install the most recent version of '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 from github 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.

A stable version of View the "bhm" package is also available from the Comprehensive R Archive Network (https://CRAN.R-project.org/package=bhm) and can be installed using R command

install.packages("bhm")

Author(s)

Bingshu E. Chen, Tian Fang, Jia Wang, Shuoshuo Liu

Maintainer: Bingshu E. Chen <bingshu.chen@queensu.ca>

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. (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 measure of treatment effects and censored survival data. Statistics in Medicine. 35, 2715-2725.

Gavanji, P., Chen, B. E. and Jiang, W.(2018). Residual Bootstrap test for interactions in biomarker threshold models with survival data. Statistics in Biosciences.

Chen, B. E. and Wang, J.(2020). Joint modelling of binary response and survival for clustered data in clinical trials. Statistics in Medicine. Vol 39. 326-339.

Liu, S. S. and Chen, B. E. (2020). Continuous threshold models with two-way interactions in sur vival analysis. Canadian Journal of Statistics.

See Also

bhm, brm, coxph, glm, survival

Examples

# fit = bhm(y~biomarker+treatment)
# print(summary(fit))

statapps/bhm documentation built on Sept. 19, 2023, 9:24 p.m.