smle_ph {smlePH}

Fit the full likelihood proportional hazards model

Description

Fit the proportional hazards model with maximum full likelihood estimation. Sieve estimation is used for estimating the baseline hazard function.

Usage

smle_ph(y, d, x)

Arguments

y

survival time (> 0).

d

right-censoring indicator, 1: observed; 0: right-censored.

x

p-dimensional covariates matrix.

Details

see Halabi et al., (2024+) for detailed method explanation.

Value

smle_ph returns a list containing the following components:

• Coef: regression estimator and its inferential results.

• Cum.hazard: baseline cumulative hazard function estimates.

References

Halabi et al., (2024+) Sieve maximum full likelihood estimation for the proportional hazards model

Examples

library(smlePH)
set.seed(111)
n = 200
beta = c(1, -1, 0.5, -0.5, 1)
p = length(beta)
beta = matrix(beta, ncol = 1)
R = matrix(c(rep(0, p^2)), ncol = p)
diag(R) = 1
mu = rep(0, p)
SD = rep(1, p)
S = R * (SD %*% t(SD))
x = MASS::mvrnorm(n, mu, S)
T = (-log(runif(n)) / (2 * exp(x %*% beta)))^(1/2)
C = runif(n, min = 0, max = 2.9)
y = apply(cbind(T,C), 1, min)
d = (T <= C)+0
ord = order(y)
y = y[ord]; x = x[ord,]; d = d[ord]
smle_ph(y = y, d = d, x = x)