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On the accelerated failure time model for current status and interval censored data

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  • Lu Tian
  • Tianxi Cai

Abstract

This paper introduces a novel approach to making inference about the regression parameters in the accelerated failure time model for current status and interval censored data. The estimator is constructed by inverting a Wald-type test for testing a null proportional hazards model. A numerically efficient Markov chain Monte Carlo based resampling method is proposed for obtaining simultaneously the point estimator and a consistent estimator of its variance-covariance matrix. We illustrate our approach with interval censored datasets from two clinical studies. Extensive numerical studies are conducted to evaluate the finite-sample performance of the new estimators. Copyright 2006, Oxford University Press.

Suggested Citation

  • Lu Tian & Tianxi Cai, 2006. "On the accelerated failure time model for current status and interval censored data," Biometrika, Biometrika Trust, vol. 93(2), pages 329-342, June.
    • Handle: RePEc:oup:biomet:v:93:y:2006:i:2:p:329-342
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    File URL: http://hdl.handle.net/10.1093/biomet/93.2.329
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    Citations

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    Cited by:

    1. Chi-Chung Wen & Chien-Tai Lin, 2011. "Analysis of Current Status Data with Missing Covariates," Biometrics, The International Biometric Society, vol. 67(3), pages 760-769, September.
    2. Stephanie Chan & Xuan Wang & Ina Jazić & Sarah Peskoe & Yingye Zheng & Tianxi Cai, 2021. "Developing and evaluating risk prediction models with panel current status data," Biometrics, The International Biometric Society, vol. 77(2), pages 599-609, June.
    3. Jue Hou & Stephanie F. Chan & Xuan Wang & Tianxi Cai, 2023. "Risk prediction with imperfect survival outcome information from electronic health records," Biometrics, The International Biometric Society, vol. 79(1), pages 190-202, March.
    4. Min Zhang & Marie Davidian, 2008. "“Smooth” Semiparametric Regression Analysis for Arbitrarily Censored Time-to-Event Data," Biometrics, The International Biometric Society, vol. 64(2), pages 567-576, June.
    5. Lianming Wang & David B. Dunson, 2011. "Semiparametric Bayes' Proportional Odds Models for Current Status Data with Underreporting," Biometrics, The International Biometric Society, vol. 67(3), pages 1111-1118, September.
    6. Sapp Stephanie & van der Laan Mark J. & Page Kimberly, 2014. "Targeted Estimation of Binary Variable Importance Measures with Interval-Censored Outcomes," The International Journal of Biostatistics, De Gruyter, vol. 10(1), pages 1-21, May.
    7. Fei Gao & Donglin Zeng & Dan‐Yu Lin, 2017. "Semiparametric estimation of the accelerated failure time model with partly interval‐censored data," Biometrics, The International Biometric Society, vol. 73(4), pages 1161-1168, December.
    8. Choi, Taehwa & Kim, Arlene K.H. & Choi, Sangbum, 2021. "Semiparametric least-squares regression with doubly-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).

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