pvaft: Estimates of univariate covariates using Accelerated Failure...

In afthd: Accelerated Failure Time for High Dimensional Data with 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(-η(x;β)))

and baseline survival function is modelled as

S_0(T)=\exp(-\exp(η_0(log(T);β_0)))

Where η and η 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)
##

afthd documentation built on Oct. 1, 2021, 5:08 p.m.