The R package BioPred offers a suite of tools for subgroup and biomarker analysis in precision medicine. Leveraging Extreme Gradient Boosting (XGBoost) along with propensity score weighting and A-learning methods, BioPred facilitates the optimization of individualized treatment rules (ITR) to streamline sub-group identification. BioPred also enables the identification of predictive biomarkers and obtaining their importance rankings. Moreover, the package provides graphical plots tailored for biomarker analysis. This tool enables clinical researchers seeking to enhance their understanding of biomarkers and patient popula-tion in drug development. ## Installation
You can install the BioPred package from GitHub using
the devtools package. If you don’t have
devtools installed, you can install it using:
install.packages("devtools")
devtools::install_github("deeplearner0731/BioPred")You can also install BioPred package from CRAN
install.packages("BioPred")XGBoostSub_con(): Function for Training XGBoost Model
with Customized Loss Function (A-loss and Weight-loss) for continuous
outcomes.XGBoostSub_bin(): Function for Training XGBoost Model
with Customized Loss Function (A-loss and Weight-loss) for binary
outcomes.XGBoostSub_sur(): Function for Training XGBoost Model
with Customized Loss Function (A-loss and Weight-loss) for time-to-event
outcomes.eval_metric_con(): Function for Evaluating
XGBoostSub_con Model Performance.eval_metric_bin(): Function for Evaluating
XGBoostSub_bin Model Performance.eval_metric_sur(): Function for Evaluating
XGBoostSub_sur Model Performance.predictive_biomarker_imp(): This function calculates
and plots the importance of biomarkers in a trained XGBoostSub_con,
XGBoostSub_bin or XGBoostSub_sur model.get_subgroup_results(): This function predicts the
treatment assignment for each patient based on a cutoff value.cdf_plot(): Cumulative Distribution Function (CDF) plot
for a biomarker.roc_bin(): AUC ROC Table for Biomarkers Associated with
Binary Outcomes.roc_bin_plot(): Generates ROC plots for different
biomarkers associated with binary outcomes.scat_cont_plot(): Scatter Plot for a Biomarker
Associated with Continuous Outcome.gam_plot(): Generates a generalized additive model
(GAM) plot for exploring the relationship between a response variable
and a biomarker.gam_ctr_plot(): Computes and plots the contrasts
between treatment and control group based on a GAM for exploring the
relationship between treatment benefit and biomarker.fixcut_con():This function conducts fixed cutoff
analysis for individual biomarker associated with continuous outcome
variables.fixcut_bin(): This function conducts fixed cutoff
analysis for individual biomarker associated with binary outcome
variables.fixcut_sur(): This function conducts fixed cutoff
analysis for individual biomarker associated with time-to-event outcome
variables.cut_perf(): This function evaluates the performance of
a predictive model at a selected cutoff point.cat_summary(): This function provides a summary of
categorical biomarkers in a dataset.subgrp_perf_pred(): This function evaluates the
performance of subgroups based on different types of response variables
in predictive cases.subgrp_perf(): This function evaluates subgroup
performance based on different types of response variables. ##
DependenciesThe BioPred package depends on the following R
packages:
pROCggplot2PropCIsxgboostpROCsurvivalmgcvsurvmineronewaytestscarBioPred.This package is maintained by Zihuan Liu. For any queries or issues, please contact me at zihuan.liu@abbvie.com.
This package is licensed under the GPL-3 License.
Contributions are welcome! Please fork the repository and submit a pull request with your changes. Make sure to follow the coding guidelines and document your code appropriately.
Special thanks to all contributors and the open-source community for their invaluable support.