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On the Accelerated Failure Time Model for Current Status and Interval Censored Data

Author

Listed:
  • Lu Tian

    (Harvard University)

  • Tianxi Cai

    (Harvard University)

Abstract

This paper introduces a novel approach to making inference about the regression parameters in the accelerated failure time (AFT) 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 (MCMC) based resampling method is proposed to simultaneously obtain the point estimator and a consistent estimator of its variance-covariance matrix. We illustrate our approach with interval censored data sets from two clinical studies. Extensive numerical studies are conducted to evaluate the finite sample performance of the new estimators.

Suggested Citation

  • Lu Tian & Tianxi Cai, 2004. "On the Accelerated Failure Time Model for Current Status and Interval Censored Data," Harvard University Biostatistics Working Paper Series 1014, Berkeley Electronic Press.
    • Handle: RePEc:bep:hvdbio:1014
    Note: oai:bepress.com:harvardbiostat-1014
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    References listed on IDEAS

    as
    1. Daniel Rabinowitz & Rebecca A. Betensky & Anastasios A. Tsiatis, 2000. "Using Conditional Logistic Regression to Fit Proportional Odds Models to Interval Censored Data," Biometrics, The International Biometric Society, vol. 56(2), pages 511-518, June.
    2. Zhezhen Jin, 2003. "Rank-based inference for the accelerated failure time model," Biometrika, Biometrika Trust, vol. 90(2), pages 341-353, June.
    3. Torben Martinussen, 2002. "Efficient estimation in additive hazards regression with current status data," Biometrika, Biometrika Trust, vol. 89(3), pages 649-658, August.
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