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Weighted Estimation of the Accelerated Failure Time Model in the Presence of Dependent Censoring

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  • Youngjoo Cho
  • Debashis Ghosh

Abstract

Independent censoring is a crucial assumption in survival analysis. However, this is impractical in many medical studies, where the presence of dependent censoring leads to difficulty in analyzing covariate effects on disease outcomes. The semicompeting risks framework offers one approach to handling dependent censoring. There are two representative estimators based on an artificial censoring technique in this data structure. However, neither of these estimators is better than another with respect to efficiency (standard error). In this paper, we propose a new weighted estimator for the accelerated failure time (AFT) model under dependent censoring. One of the advantages in our approach is that these weights are optimal among all the linear combinations of the previously mentioned two estimators. To calculate these weights, a novel resampling-based scheme is employed. Attendant asymptotic statistical results for the estimator are established. In addition, simulation studies, as well as an application to real data, show the gains in efficiency for our estimator.

Suggested Citation

  • Youngjoo Cho & Debashis Ghosh, 2015. "Weighted Estimation of the Accelerated Failure Time Model in the Presence of Dependent Censoring," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-22, April.
    • Handle: RePEc:plo:pone00:0124381
    DOI: 10.1371/journal.pone.0124381
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    References listed on IDEAS

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    1. Debashis Ghosh & D. Y. Lin, 2003. "Semiparametric Analysis of Recurrent Events Data in the Presence of Dependent Censoring," Biometrics, The International Biometric Society, vol. 59(4), pages 877-885, December.
    2. Honore, Bo E. & Powell, James L., 1994. "Pairwise difference estimators of censored and truncated regression models," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 241-278.
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    1. Carroll, Rachel & Lawson, Andrew B. & Jackson, Chandra L. & Zhao, Shanshan, 2017. "Assessment of spatial variation in breast cancer-specific mortality using Louisiana SEER data," Social Science & Medicine, Elsevier, vol. 193(C), pages 1-7.
    2. Qiangru Shen & Xun Xie & Gen Li & Lan Wu & Le Zhao & Zhen Yang, 2024. "Study on ring-road incident duration based on latent class accelerated hazard model," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-14, August.

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