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Score Estimating Equations from Embedded Likelihood Functions Under Accelerated Failure Time Model

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  • Jing Ning
  • Jing Qin
  • Yu Shen

Abstract

The semiparametric accelerated failure time (AFT) model is one of the most popular models for analyzing time-to-event outcomes. One appealing feature of the AFT model is that the observed failure time data can be transformed to identically independent distributed random variables without covariate effects. We describe a class of estimating equations based on the score functions for the transformed data, which are derived from the full likelihood function under commonly used semiparametric models such as the proportional hazards or proportional odds model. The methods of estimating regression parameters under the AFT model can be applied to traditional right-censored survival data as well as more complex time-to-event data subject to length-biased sampling. We establish the asymptotic properties and evaluate the small sample performance of the proposed estimators. We illustrate the proposed methods through applications in two examples.

Suggested Citation

  • Jing Ning & Jing Qin & Yu Shen, 2014. "Score Estimating Equations from Embedded Likelihood Functions Under Accelerated Failure Time Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1625-1635, December.
    • Handle: RePEc:taf:jnlasa:v:109:y:2014:i:508:p:1625-1635
    DOI: 10.1080/01621459.2014.946034
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    References listed on IDEAS

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    1. Jing Ning & Jing Qin & Yu Shen, 2010. "Non‐parametric tests for right‐censored data with biased sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(5), pages 609-630, November.
    2. Zhezhen Jin, 2003. "Rank-based inference for the accelerated failure time model," Biometrika, Biometrika Trust, vol. 90(2), pages 341-353, June.
    3. Zeng, Donglin & Lin, D.Y., 2007. "Efficient Estimation for the Accelerated Failure Time Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1387-1396, December.
    4. O. Davidov & G. Iliopoulos, 2013. "Convergence of Luo and Tsai's iterative algorithm for estimation in proportional likelihood ratio models," Biometrika, Biometrika Trust, vol. 100(3), pages 778-780.
    5. Shen, Yu & Ning, Jing & Qin, Jing, 2009. "Analyzing Length-Biased Data With Semiparametric Transformation and Accelerated Failure Time Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1192-1202.
    6. Jing Ning & Jing Qin & Yu Shen, 2011. "Buckley–James-Type Estimator with Right-Censored and Length-Biased Data," Biometrics, The International Biometric Society, vol. 67(4), pages 1369-1378, December.
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    Cited by:

    1. James H. McVittie & Ana F. Best & David B. Wolfson & David A. Stephens & Julian Wolfson & David L. Buckeridge & Shahinaz M. Gadalla, 2023. "Survival Modelling for Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis Using Electronic Health Records," International Statistical Review, International Statistical Institute, vol. 91(1), pages 72-87, April.
    2. Jin Piao & Jing Ning & Yu Shen, 2019. "Semiparametric model for bivariate survival data subject to biased sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 409-429, April.

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