pvaft {afthd}

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

m

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

n

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

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.

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.

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.

Author(s)

Atanu Bhattacharjee, Gajendra Kumar Vishwakarma and Pragya Kumari

See Also

wbysuni,wbysmv, rglaft

Examples

##
data(hdata)
pvaft(9,30,STime="os",Event="death",0.1,hdata)
##