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

Description

Given the arguments, it can evaluate the value of the objective function used by the optimization algorithms for determining the optimal regression parameters and baseline hazard values.

Usage

weight_estimator_BLH(survDataT, geDataT, weights_baselineH, q, s, a, b, groups)

Arguments

survDataT

The survival data of the patient set passed on by the user. It takes on the form of a data frame with at least have the following columns True_STs and censored, corresponding to the observed survival times and the censoring status of the subjects consecutively. Censored patients are assigned a 1 while patients who experience an event are assigned 1.

geDataT

The co-variate data (gene expression or aCGH, etc...) of the patient set passed on by the user. It is a matrix with the co-variates in the columns and the subjects in the rows. Each cell corresponds to that rowth subject's columnth co-variate's value.

weights_baselineH

A single vector with the initial values of the baseline hazards followed by the weights(regression coefficients) for the co-variates.

One of the two parameters on the prior distribution used on the weights (regression coefficients) in the model.

The second of the two parameters on the prior distribution used on the weights (regression coefficients) in the model.

The shape parameter for the gamma distribution used as a prior on the baseline hazards.

The scale parameter for the gamma distribution used as a prior on the baseline hazards.

groups

The number of partitions along the time axis for which a different baseline hazard is to be assigned. This number should be the same as the number of initial values passed for the baseline hazards in the beginning of the weights_baselineH argument.

Value

A single numerical value corresponding to the value of the objective function with the given regression coefficients and baseline hazard values.

References

The basic model is based on the Cox regression model as first introduced by Sir David Cox in: Cox,D.(1972).Regression models & life tables. Journal of the Royal Society of Statistics, 34(2), 187-220. The extension of the Cox model to its stepwise form was adapted from: Ibrahim, J.G, Chen, M.-H. & Sinha, D. (2005). Bayesian Survival Analysis (second ed.). NY: Springer. as well as Kaderali, Lars.(2006) A Hierarchial Bayesian Approach to Regression and its Application to Predicting Survival Times in Cancer Patients. Aachen: Shaker The prior on the regression coefficients was adopted from: Mazur, J., Ritter,D.,Reinelt, G. & Kaderali, L. (2009). Reconstructing Non-Linear dynamic Models of Gene Regulation using Stochastic Sampling. BMC Bioinformatics, 10(448).

Examples

Run this code

data(Bergamaschi)
data(survData)
weight_estimator_BLH(survDataT=survData[1:10, 9:10], geDataT=Bergamaschi[1:10 , 1:2],weights_baselineH=c(0.1,0.2,0.3,rep(0,ncol(Bergamaschi))), q=1, s=1, a=1.5, b=0.3, groups=3)