add.haz {SurvSparse}

Additive hazards model with sparse longitudinal covariates

Description

Regression analysis of additive hazards model with sparse longitudinal covariates. Three different weighting schemes are provided to impute the missing values.

Usage

add.haz(data, n, tau, h, method)

Arguments

data

An object of class tibble. The structure of the tibble must be: tibble(id = id, X = failure time, covariates = observation for covariates, obs_times = observation times, delta = censoring indicator).

n

An object of class integer. The sample size.

tau

An object of class numeric. The pre-specified time endpoint.

h

An object of class vector. If use auto bandwidth selection, the structure of the vector must be: h = c(the maximum bandwidth, the minimum bandwidth, the number of bandwidth divided). If use fixed bandwidth, h is the chosen bandwidth.

method

An object of class integer. If use weighted LVCF, method = 1. If use half kernel, method = 2. If use full kernel, method = 3.

Value

a list with the following elements:

est

The estimation for the corresponding parameters.

se

The estimation for the standard error of the estimated parameters.

References

Sun, Z. et al. (2022) <doi:10.1007/s10985-022-09548-6>

Examples

library(gaussquad)
library(dplyr)
library(nleqslv)
library(MASS)
n=500
lqrule64 <- legendre.quadrature.rules(64)[[64]]
simdata <- function(alpha,beta ) {
cen=1
nstep=20
Sigmat_z <- exp(-abs(outer(1:nstep, 1:nstep, "-")) / nstep)
z  <-   c(mvrnorm(  1, c(1: nstep)/2, Sigmat_z  ))
left_time_points <- (0:(nstep - 1)) / nstep
z_fun <- stepfun(left_time_points, c(0,z  ))
lam_fun <- function(tt) {  alpha(tt)+beta*z_fun(tt)}
u <- runif(1)
fail_time <- nleqslv( 0 , function(ttt)
 legendre.quadrature(lam_fun,
                   lower = 0,
                   upper = ttt,
                   lqrule64) + log(u))$x

X <- min(fail_time, cen)
obs=rpois(1,5)+1
tt= sort(runif(obs, min = 0, max = 1))
obs_times <- tt[which(tt<=cen)]
if (length(obs_times) == 0)
 obs_times <- cen
 covariates_obscov <-z_fun(obs_times)
 return( tibble(X = X,delta = fail_time < cen,
 covariates = covariates_obscov,obs_times = obs_times, censoring = cen  )  ) }
data <- replicate(n, simdata(alpha = function(tt)  tt, 1  ),
              simplify = FALSE ) %>% bind_rows(.id = "id")

add.haz(data,n,1,n^(-0.5),3)