uni.Wald {compound.Cox}

Univariate Cox Wald test

Description

Univariate significance analyses via the Wald tests (Witten & Tibshirani 2010; Emura et al. 2019) based on association between individual features and survival.

Usage

uni.Wald(t.vec, d.vec, X.mat)

Arguments

t.vec

Vector of survival times (time to either death or censoring)

d.vec

Vector of censoring indicators, 1=death, 0=censoring

X.mat

n by p matrix of covariates, where n is the sample size and p is the number of covariates

Details

Wald test

Value

beta

Estimated regression coefficients

Z

Z-value for testing H_0: beta=0 (Wald test)

P

P-value for testing H_0: beta=0 (Wald test)

Author(s)

Takeshi Emura

References

Emura T, Matsui S, Chen HY (2019). compound.Cox: Univariate Feature Selection and Compound Covariate for Predicting Survival, Computer Methods and Programs in Biomedicine 168: 21-37.

Witten DM, Tibshirani R (2010) Survival analysis with high-dimensional covariates. Stat Method Med Res 19:29-51

Examples

data(Lung)
t.vec=Lung$t.vec[Lung$train==TRUE]
d.vec=Lung$d.vec[Lung$train==TRUE]
X.mat=Lung[Lung$train==TRUE,-c(1,2,3)]
uni.Wald(t.vec, d.vec, X.mat)