deepAFT-package: Deep learning for accelerated failure time data

In statapps/deepAFT: Deep learning for AFT model in survival

Deep learning for accelerated failure time data

Description

A deep learning package for survival analysis using accelerated failure time (AFT) models.

Details

"deepAFT" is a R package for deep learning accerelated failure time Models. Please use the following steps to install the most recent version of 'deepAFT' package:

1. First, you need to install the 'devtools' package. You can skip this step if you have 'devtools' installed in your R. Invoke R and then type

install.packages("devtools")

2. Load the devtools package.

library(devtools)

3. Install "deepAFT" package from github with R commond

install_github("statapps/deepAFT")

"deepAFT" uses deep learning methods to fir an accerelated failure time model for survival data with high dimensional biomarker variables or other covariates.

A stable version of the "deepAFT" is now part of an R package "dnn" available from the Comprehensive R Archive Network (https://CRAN.R-project.org/package=dnn) and can be installed using R command

install.packages("dnn") library(dnn) help("deepAFT")

Note

For now, deepAFT works only for right censored data. Methods for other types of censoring will be developed soon.

Author(s)

Patrick Norman, Wanlux, Li, Wenyu Jiang and Bingshu E. Chen

References

Buckley, J. and James, I. (1979). Linear regression with cencored data. Biometrika, 66, page 429-436.

Norman, P. and Chen, B. E. (2018). DeepAFAT: A nonparametric accelerated failure time model with artifical neural network. M. Sc. thesis report. Department of Public Health Sciences, Queen's University, Canada.

Norman, P. Li, W., Jiang, W. and Chen, B. E. (2024). deepAFT: A nonlinear accelerated failure time model with artificial neural network. Statistics in Medicine. Published June 18, 2024.

Chollet, F. and Allaire J. J. (2017). Deep learning with R. Manning.

See Also

coxph, glm, survival

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
# fit = deepAFT(Surv(time, status) ~ x)
# print(fit)
#  

statapps/deepAFT documentation built on June 25, 2024, 2:17 a.m.