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Fitting Accelerated Failure Time Models in Routine Survival Analysis with R Package aftgee

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  • Chiou, Sy Han
  • Kang, Sangwook
  • Yan, Jun

Abstract

Accelerated failure time (AFT) models are alternatives to relative risk models which are used extensively to examine the covariate effects 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 aftgee 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 efficient 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.

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  • Chiou, Sy Han & Kang, Sangwook & Yan, Jun, 2014. "Fitting Accelerated Failure Time Models in Routine Survival Analysis with R Package aftgee," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i11).
    • Handle: RePEc:jss:jstsof:v:061:i11
    DOI: http://hdl.handle.net/10.18637/jss.v061.i11
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    References listed on IDEAS

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    1. Varadhan, Ravi & Gilbert, Paul, 2009. "BB: An R Package for Solving a Large System of Nonlinear Equations and for Optimizing a High-Dimensional Nonlinear Objective Function," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i04).
    2. B. M. Brown & You-Gan Wang, 2005. "Standard errors and covariance matrices for smoothed rank estimators," Biometrika, Biometrika Trust, vol. 92(1), pages 149-158, March.
    3. Lynn M. Johnson & Robert L. Strawderman, 2009. "Induced smoothing for the semiparametric accelerated failure time model: asymptotics and extensions to clustered data," Biometrika, Biometrika Trust, vol. 96(3), pages 577-590.
    4. Wang, You-Gan & Fu, Liya, 2011. "Rank regression for accelerated failure time model with clustered and censored data," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2334-2343, July.
    5. He, Wenqing & Xiong, Juan & Yi, Grace Y., 2012. "SIMEX R Package for Accelerated Failure Time Models with Covariate Measurement Error," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 46(c01).
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    Cited by:

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    2. Byungtae Seo & Sangwook Kang, 2023. "Accelerated failure time modeling via nonparametric mixtures," Biometrics, The International Biometric Society, vol. 79(1), pages 165-177, March.
    3. Marc Buyse & Everardo D. Saad & Tomasz Burzykowski & Julien Péron, 2020. "Assessing Treatment Benefit in Immuno-oncology," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 83-103, July.
    4. Lea Kats & Malka Gorfine, 2023. "An accelerated failure time regression model for illness–death data: A frailty approach," Biometrics, The International Biometric Society, vol. 79(4), pages 3066-3081, December.

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