ALassoSurvIC (version 0.1.1)

baseline: Obtaining the nonparametric maximum likelihood estimate (NPMLE) for the baseline cumulative hazard function

Description

Extracting the NPMLE for the baseline cumulative hazard function from the input object. The input object must be the objects returned by the alacoxIC function or the unpencoxIC function. The support set over which the cumulative hazard increases is the same as that of the nonparametric maximum likelihood estimator, characterized by Alioum and Commenges (1996). The full details are available in Li et al. (2019).

Usage

# S3 method for default
baseline(object, ...)

Value

A list with components:

support

The maximal intersections with a finite upper endpoint.

lambda

The jump sizes over the support set.

cum.lambda

The NPMLE of the baseline cumulative hazard function.

Arguments

...

for S4 method only.

object

the object must be the object retruned by the alacoxIC function or the unpencoxIC function.

Details

The estimator for the baseline cumulative hazard function increases only on some support sets, so called maximal intersections, and the NPMLE is indifferent to how it increases on the support sets. The definition of maximal intersections and other details are available in Alioum and Commenges (1996) and Li et al. (2019).

References

Alioum, A. and Commenges, D. (1996). A proportional hazards model for arbitrarily censored and truncated data. Biometrics 52, 512-524.

Li, C., Pak, D., & Todem, D. (2019). Adaptive lasso for the Cox regression with interval censored and possibly left truncated data. Statistical methods in medical research. tools:::Rd_expr_doi("10.1177/0962280219856238")

See Also

alacoxIC; unpencoxIC

Examples

library(ALassoSurvIC)

### Display the hazard function for the interval censored data
data(ex_ICLT) # the 'virtual' data having 100 subjects and 6 covariates
lowerIC <- ex_ICLT$lowerIC
upperIC <- ex_ICLT$upperIC
trunc <- ex_ICLT$trunc
X <- ex_ICLT[, -c(1:3)]
result <- unpencoxIC(lowerIC, upperIC, X, trunc)
baseline(result)