spcox {NPCox}

Nonparametric and semiparametric Cox regression model.

Description

Estimation of proportional hazards (PH) model with time-varying coefficients and constant coefficients.

Usage

spcox(
  cva_cons,
  cva_time,
  delta,
  obstime,
  SE = FALSE,
  bandwidth = FALSE,
  resamp = 100
)

Arguments

cva_cons

Covariate Z1 with constant coefficeint c in h(t) = h0(t)exp(c'Z1 + b(t)'Z2)

cva_time

Covariate Z2 with time-varying coefficeint b(t) in h(t) = h0(t)exp(c'Z1 + b(t)'Z2)

delta

Right censoring indicator for the model

obstime

The observed time = min(censoring time, observed failure time)

SE

Whether or not the estimation of standard error through resampling method will be done. The default value is FALSE.

bandwidth

Bandwidth for kernel function, which can be specified. The default value is FALSE and can be selected through least prediction error over all subjects.

resamp

Number of resampling for estimation of pointwise standard error. The default value is 100.

Details

This is some description of this function.

'spcox' is designed for PH model with both time-varying and constant coefficients, h(t) = h0(t)exp(b(t)'Z1 + c*Z2), providing estimation of b(t), c and their standard errors.

Value

a list that contain the estimation result of both temporal and constant coefficients, standard error estimation, selected or predesigned bandwidth, dataset, unconverged time points.

Examples

data(pbc)
pbc = pbc[(pbc$time < 3000) & (pbc$time > 800), ] 
Z1  = as.matrix(pbc[,5])
Z2  = as.matrix(pbc[,c('albumin')])
colnames(Z1) = c('age')
colnames(Z2) = c('albumin')
del = pbc$status
tim = pbc$time
res1 = spcox(cva_cons = Z1, cva_time = Z2, delta = del, obstime = tim, bandwidth = 500)