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Conditional MLE for the proportional hazards model with left-truncated and interval-censored data

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  • Shen, Pao-sheng

Abstract

We consider conditional maximum likelihood estimator (cMLE) for the proportional hazards model with left-truncated and interval-censored data. We show that when the covariates are discrete the cMLE is the MLE, and under some regularity conditions the cMLE for the regression parameter is asymptotically normal and efficient.

Suggested Citation

  • Shen, Pao-sheng, 2015. "Conditional MLE for the proportional hazards model with left-truncated and interval-censored data," Statistics & Probability Letters, Elsevier, vol. 100(C), pages 164-171.
    • Handle: RePEc:eee:stapro:v:100:y:2015:i:c:p:164-171
    DOI: 10.1016/j.spl.2015.02.015
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    1. Wei Pan & Rick Chappell, 2002. "Estimation in the Cox Proportional Hazards Model with Left-Truncated and Interval-Censored Data," Biometrics, The International Biometric Society, vol. 58(1), pages 64-70, March.
    2. Els Goetghebeur & Louise Ryan, 2000. "Semiparametric Regression Analysis of Interval-Censored Data," Biometrics, The International Biometric Society, vol. 56(4), pages 1139-1144, December.
    3. Wei Pan, 2000. "A Multiple Imputation Approach to Cox Regression with Interval-Censored Data," Biometrics, The International Biometric Society, vol. 56(1), pages 199-203, March.
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    Cited by:

    1. Pao-sheng Shen & Yingwei Peng & Hsin-Jen Chen & Chyong-Mei Chen, 2022. "Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(1), pages 68-88, January.

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