The package is the R-version of the C-based software CASPAR (Kaderali,2006). It is meant to help predict survival times in the presence of high-dimensional explanatory co-variates. 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 co-variates 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.
● Data Source:
BioConductor
● Keywords: Piecewise baseline hazard Cox regression model, survival analysis
● Alias: RCASPAR, RCASPAR-package
●
4 images
|
STpredictor_BLH
(Package: RCASPAR) :
Predicts the survival times of the validation set based on the regression coefficients and baseline hazards determined according to the Piecewise baseline hazard Cox
This function uses the training set to estimated the best regression coefficients, and baseline hazards describing the data according to the piecewise baseline hazard Cox regression model. It then takes them and uses them to predict the survival times of the validation set, which are determined as the mean value of the p.d.f. of the survival time, as a continuous random variable, given the co-variate values of that subject.
● Data Source:
BioConductor
● Keywords: piecewise baseline hazard Cox PH regression model, survival time prediction
● Alias: STpredictor_BLH
●
0 images
|
|
Using the full data provided by the user, this function splits the data set k times, into a smaller validation set, and a much bigger training set. The regression coefficients of the model are estimated from the training set and used to predict the survival times of the validation set. The patients can then be split into patients two groups around a cut off value also specified by the user.
● Data Source:
BioConductor
● Keywords: cross validation, piecewise baseline hazard Cox PH model
● Alias: STpredictor_xvBLH
●
0 images
|
|
Given the necessary data, this function calculates the derivative of the objective function without a w.r.t. the baseline hazards and weights(regression coefficients) in the model to be used in gradient-based optimization algorithms.
● Data Source:
BioConductor
● Keywords: Cox regression model, gradient of objective
● Alias: deriv_weight_estimator_BLH
●
0 images
|
|
Given the necessary data, this function calculates the derivative of the objective function without a prior on the regression coefficients w.r.t. the baseline hazards and weights(regression coefficients) in the model to be used in gradient-based optimization algorithms. This is sometimes necessary to get the optimization algorithm out of a peaked origin where it could start.
● Data Source:
BioConductor
● Keywords: cox regression, derivative of objective function
● Alias: deriv_weight_estimator_BLH_noprior
●
0 images
|
kmplt
(Package: RCASPAR) :
Plot Kaplan Meier curve
This function plots the survival curve for the provided data set as a Kaplan Meier plot. It can only be used for visualization and returns no numeric values.
● Data Source:
BioConductor
● Keywords: Kaplan Meier, Survival function
● Alias: kmplt
●
1 images
|
This function can plot the KM curves estimated for $2-3$ patients simultaneously for sake of easier comparison.
● Data Source:
BioConductor
● Keywords: Kaplan-Meier estimator
● Alias: kmplt_svrl
●
1 images
|
logrnk
(Package: RCASPAR) :
Performs Log Rank test on the long and short patient sets
This function performs a Chi-square test on the long and short subject sets to determine if their is a significant difference between the survival times in both sets. It returns the p-value.
● Data Source:
BioConductor
● Keywords: log rank test
● Alias: logrnk
●
0 images
|
pltgamma
(Package: RCASPAR) :
Plotting the gamma distribution of shape parameter
This function allows the user to visualize the shape of the gamma distribution used as a prior on the baseline hazards in the functions ending with "_BLH"
● Data Source:
BioConductor
● Keywords: gamma distribution
● Alias: pltgamma
●
1 images
|
This function helps visualize the effect of the parameters q and s on the prior distribution used on the weights.
● Data Source:
BioConductor
● Keywords: Lq-Norm prior
● Alias: pltprior
●
1 images
|
providing 
.