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Robust estimation in accelerated failure time models

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  • Sanjoy K. Sinha

    (Carleton University)

Abstract

The accelerated failure time model is widely used for analyzing censored survival times often observed in clinical studies. It is well-known that the ordinary maximum likelihood estimators of the parameters in the accelerated failure time model are generally sensitive to potential outliers or small deviations from the underlying distributional assumptions. In this paper, we propose and explore a robust method for fitting the accelerated failure time model to survival data by bounding the influence of outliers in both the outcome variable and associated covariates. We also develop a sandwich-type variance–covariance function for approximating the variances of the proposed robust estimators. The finite-sample properties of the estimators are investigated based on empirical results from an extensive simulation study. An application is provided using actual data from a clinical study of primary breast cancer patients.

Suggested Citation

  • Sanjoy K. Sinha, 2019. "Robust estimation in accelerated failure time models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 52-78, January.
    • Handle: RePEc:spr:lifeda:v:25:y:2019:i:1:d:10.1007_s10985-018-9421-z
    DOI: 10.1007/s10985-018-9421-z
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    References listed on IDEAS

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    1. Locatelli, Isabella & Marazzi, Alfio & Yohai, Victor J., 2011. "Robust accelerated failure time regression," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 874-887, January.
    2. Cantoni E. & Ronchetti E., 2001. "Robust Inference for Generalized Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1022-1030, September.
    3. Alessandra Nardi & Michael Schemper, 1999. "New Residuals for Cox Regression and Their Application to Outlier Screening," Biometrics, The International Biometric Society, vol. 55(2), pages 523-529, June.
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    Cited by:

    1. Adam Braima S. Mastor & Abdulaziz S. Alghamdi & Oscar Ngesa & Joseph Mung’atu & Christophe Chesneau & Ahmed Z. Afify, 2023. "The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data," Mathematics, MDPI, vol. 11(2), pages 1-26, January.
    2. Jad Beyhum & Ingrid Keilegom, 2023. "Robust censored regression with $$\ell _1$$ ℓ 1 -norm regularization," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 146-162, March.

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