pvaft: Estimates of univariate covariates using Accelerated Failure time (AFT) model without MCMC.

Description

Provides list of covariates and their estimates of parametric AFT model with smooth time functions, whose p value is less than chosen value (by default p=1 that is all chosen covariates come in result). Using AFT model for univariate in high dimensional data without MCMC.

Usage

pvaft(m, n, STime, Event, p = 1, data)

Arguments

Starting column number of covariates of study in high dimensional entered data.

Ending column number of covariates of study in high dimensional entered data.

STime

name of survival time in data.

Event

name of event in data. 0 is for censored and 1 for occurrence of event.

p-value, to make restriction for selection of covariates, default value is 1.

data

High dimensional gene expression data that contains event status, survival time and and set of covariates.

Value

Matrix that contains survival information of selected covariates(selected from chosen columns whose p value is <= p) on AFT model. Result shows together for all covariates chosen from column m to n.

Details

Survival time T for covariate x, is modelled as AFT model using $$S(T|x)=S_0(T\exp(-\eta(x;\beta)))$$ and baseline survival function is modelled as $$S_0(T)=\exp(-\exp(\eta_0(log(T);\beta_0)))$$ Where $\eta$ and $\eta$ are linear predictor.

Examples

Run this code

# NOT RUN {
##
data(hdata)
pvaft(9,30,STime="os",Event="death",0.1,hdata)
##
# }