hdcoxma: High dimensional multivariate cox proportional model with...

In autohd: High Dimensional Bayesian Survival Mediation Analysis

Description

Given the dimension of variables and survival information the function filters significant variables by fitting multivariate Cox PH with 5 variables at a time. Further, it performs mediation analysis among the significant variables and provides handful variables with their alpha.a values which are mediator model exposure coefficients and beta.a coefficients.

Usage

hdcoxma(m, n, survdur, event, ths, b, d, data)

Arguments

m	Starting column number from where high dimensional variates to be selected.
n	Ending column number till where high dimensional variates to be selected.
survdur	"Column/Variable name" consisting duration of survival.
event	"Column/Variable name" consisting survival event.
ths	A numeric between 0 to 100.
b	Number of MCMC iterations to burn.
d	Number of draws for the iterations.
data	High dimensional data containing survival observations and high dimensional covariates.

Value

Data frame containing the beta and alpha values of active variables among the significant variables.

Examples

hdcoxma(m=8,n=106,survdur="os",event="death",ths=0.02,b=10,d=10,data=hnscc2)

autohd documentation built on May 10, 2021, 9:10 a.m.