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RCASPAR (version 1.18.0)

A package for survival time prediction based on a piecewise baseline hazard Cox regression model.

Description

The package is the R-version of the C-based software \bold{CASPAR} (Kaderali,2006: \url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}). It is meant to help predict survival times in the presence of high-dimensional explanatory covariates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant covariates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine.

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Version

Version

1.18.0

License

GPL (>=3)

Maintainer

Douaa Mugahid

Last Published

February 15th, 2017

Functions in RCASPAR (1.18.0)

A special version of STpredictor.BLH used within k-xv to predict the survival times of the kth validation group in the cross validation step.

Plotting the gamma distribution of shape parameter

A function that calculates the area under a curve based on the Simposon algorithm

STpredictor_xvBLH

This function performs a cross validation on the full data set to help predict the survival times of the patients using the piecewise baseline hazard PH Cox model.

RCASPAR-package

A package for survival time prediction based on a piecewise baseline hazard Cox regression model.

A function that calculates the area under a curve based on the Simposon algorithm

Survial data of 82 patients

weight_estimator_BLH

Returns the value of the objective function used for optimizing for the regression parameters and baseline hazards in the model.

Performs Log Rank test on the long and short patient sets

deriv_weight_estimator_BLH_noprior

A function that gives the derivative of the objective function of the model for gradient-based optimization algorithms without including the prior on the regression coefficients.

Generates the ROC curve at a given time point given the observed and predicted survival data in the presence of censored subjects.

Plot Kaplan Meier curve

deriv_weight_estimator_BLH

A function that gives the derivative of the objective function of the model for gradient-based optimization algorithms.

Optimization for the regression coefficients and baseline hazards that maximize the partial likelihood in our PW Cox PH regression model.

STpredictor_BLH

Predicts the survival times of the validation set based on the regression coefficients and baseline hazards determined according to the Piecewise baseline hazard Cox regression model.

A function that plots the KM curves of $2-3$ patient sets in one graph.

A function to visualize the shape of the prior on the weights with the chosen q and s parameters.

A function that calculates the area under a curve constructed from plotting the area under a ROC curve at the corresponding time point at which it was generated.

Gene expression data of 82 patients with 10 genes as covariates

weight_estimator_BLH_noprior

Returns the value of the objective function used for optimizing for the regression parameters and baseline hazards in the model, without including the prior on the regression coefficients.

Plot Kaplan Meier curve