Fitting accelerated failure time models in routine survival analysis with R package aftgee

Sy Han Chiou, Sangwook Kang, Sangwook Kang

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Accelerated failure time (AFT) models are alternatives to relative risk models which are used extensively to examine the covariate efiects on event times in censored data regression. Nevertheless, AFT models have been much less utilized in practice due to lack of reliable computing methods and software. This paper describes an R package aft- gee that implements recently developed inference procedures for AFT models with both the rank-based approach and the least squares approach. For the rank-based approach, the package allows various weight choices and uses an induced smoothing procedure that leads to much more eficient computation than the linear programming method. With the rank-based estimator as an initial value, the generalized estimating equation approach is used as an extension of the least squares approach to the multivariate case. Additional sampling weights are incorporated to handle missing data needed as in case-cohort studies or general sampling schemes. A simulated dataset and two real life examples from biomedical research are employed to illustrate the usage of the package.

Original languageEnglish
Pages (from-to)1-23
Number of pages23
JournalJournal of Statistical Software
Volume61
Issue number11
DOIs
Publication statusPublished - 2014 Jan 1

Fingerprint

Accelerated Failure Time Model
Survival Analysis
Least Squares
Computing Methods
Cohort Study
Generalized Estimating Equations
Relative Risk
Censored Data
Sampling
Missing Data
Covariates
Smoothing
Linear programming
Regression
Estimator
Software
Alternatives
Survival analysis
Model

All Science Journal Classification (ASJC) codes

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

@article{77b5bdd01d694be381285c6a6662e6cd,
title = "Fitting accelerated failure time models in routine survival analysis with R package aftgee",
abstract = "Accelerated failure time (AFT) models are alternatives to relative risk models which are used extensively to examine the covariate efiects on event times in censored data regression. Nevertheless, AFT models have been much less utilized in practice due to lack of reliable computing methods and software. This paper describes an R package aft- gee that implements recently developed inference procedures for AFT models with both the rank-based approach and the least squares approach. For the rank-based approach, the package allows various weight choices and uses an induced smoothing procedure that leads to much more eficient computation than the linear programming method. With the rank-based estimator as an initial value, the generalized estimating equation approach is used as an extension of the least squares approach to the multivariate case. Additional sampling weights are incorporated to handle missing data needed as in case-cohort studies or general sampling schemes. A simulated dataset and two real life examples from biomedical research are employed to illustrate the usage of the package.",
author = "Chiou, {Sy Han} and Sangwook Kang and Sangwook Kang",
year = "2014",
month = "1",
day = "1",
doi = "10.18637/jss.v061.i11",
language = "English",
volume = "61",
pages = "1--23",
journal = "Journal of Statistical Software",
issn = "1548-7660",
publisher = "University of California at Los Angeles",
number = "11",

}

Fitting accelerated failure time models in routine survival analysis with R package aftgee. / Chiou, Sy Han; Kang, Sangwook; Kang, Sangwook.

In: Journal of Statistical Software, Vol. 61, No. 11, 01.01.2014, p. 1-23.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Fitting accelerated failure time models in routine survival analysis with R package aftgee

AU - Chiou, Sy Han

AU - Kang, Sangwook

AU - Kang, Sangwook

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Accelerated failure time (AFT) models are alternatives to relative risk models which are used extensively to examine the covariate efiects on event times in censored data regression. Nevertheless, AFT models have been much less utilized in practice due to lack of reliable computing methods and software. This paper describes an R package aft- gee that implements recently developed inference procedures for AFT models with both the rank-based approach and the least squares approach. For the rank-based approach, the package allows various weight choices and uses an induced smoothing procedure that leads to much more eficient computation than the linear programming method. With the rank-based estimator as an initial value, the generalized estimating equation approach is used as an extension of the least squares approach to the multivariate case. Additional sampling weights are incorporated to handle missing data needed as in case-cohort studies or general sampling schemes. A simulated dataset and two real life examples from biomedical research are employed to illustrate the usage of the package.

AB - Accelerated failure time (AFT) models are alternatives to relative risk models which are used extensively to examine the covariate efiects on event times in censored data regression. Nevertheless, AFT models have been much less utilized in practice due to lack of reliable computing methods and software. This paper describes an R package aft- gee that implements recently developed inference procedures for AFT models with both the rank-based approach and the least squares approach. For the rank-based approach, the package allows various weight choices and uses an induced smoothing procedure that leads to much more eficient computation than the linear programming method. With the rank-based estimator as an initial value, the generalized estimating equation approach is used as an extension of the least squares approach to the multivariate case. Additional sampling weights are incorporated to handle missing data needed as in case-cohort studies or general sampling schemes. A simulated dataset and two real life examples from biomedical research are employed to illustrate the usage of the package.

UR - http://www.scopus.com/inward/record.url?scp=84910660104&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84910660104&partnerID=8YFLogxK

U2 - 10.18637/jss.v061.i11

DO - 10.18637/jss.v061.i11

M3 - Article

AN - SCOPUS:84910660104

VL - 61

SP - 1

EP - 23

JO - Journal of Statistical Software

JF - Journal of Statistical Software

SN - 1548-7660

IS - 11

ER -