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Efficient estimation for semiparametric cure models with interval-censored data

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  • Hu, Tao
  • Xiang, Liming

Abstract

This paper is concerned with the analysis of interval-censored survival data in the presence of a non-negligible cure fraction using semiparametric non-mixture cure models. We propose a spline-based sieve estimation method which overcomes numerical difficulties encountered in the existing semiparametric maximum likelihood estimation for the unknown nonparametric component in models. This method is easy to implement using the sequential quadratic programming technique. Under certain regularity conditions, we show the consistency, asymptotic normality and semiparametric efficiency of the proposed estimators for parameters. For the nonparametric component, our estimator has an explicit convergence rate, higher than that conjectured by Liu and Shen (2009) [16]. We conduct extensive simulation studies to evaluate the finite-sample performance of the method proposed. The results suggest that our method produces generally more efficient estimators than the existing method. The application of the method is illustrated with data from a study of smoking cessation.

Suggested Citation

  • Hu, Tao & Xiang, Liming, 2013. "Efficient estimation for semiparametric cure models with interval-censored data," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 139-151.
    • Handle: RePEc:eee:jmvana:v:121:y:2013:i:c:p:139-151
    DOI: 10.1016/j.jmva.2013.06.006
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    References listed on IDEAS

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    9. Tsodikov A.D. & Ibrahim J.G. & Yakovlev A.Y., 2003. "Estimating Cure Rates From Survival Data: An Alternative to Two-Component Mixture Models," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1063-1078, January.
    10. Zeng, Donglin & Yin, Guosheng & Ibrahim, Joseph G., 2006. "Semiparametric Transformation Models for Survival Data With a Cure Fraction," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 670-684, June.
    11. Mao, Meng & Wang, Jane-Ling, 2010. "Semiparametric Efficient Estimation for a Class of Generalized Proportional Odds Cure Models," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 302-311.
    12. Ying Zhang & Lei Hua & Jian Huang, 2010. "A Spline‐Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval‐Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 338-354, June.
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    Cited by:

    1. Xu, Yang & Zhao, Shishun & Hu, Tao & Sun, Jianguo, 2021. "Variable selection for generalized odds rate mixture cure models with interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    2. Chen, Yurong & Feng, Yanqin & Sun, Jianguo, 2015. "Regression analysis of multivariate current status data with auxiliary covariates under the additive hazards model," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 34-45.
    3. Hu, Tao & Xiang, Liming, 2016. "Partially linear transformation cure models for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 257-269.
    4. N. Balakrishnan & M. V. Koutras & F. S. Milienos & S. Pal, 2016. "Piecewise Linear Approximations for Cure Rate Models and Associated Inferential Issues," Methodology and Computing in Applied Probability, Springer, vol. 18(4), pages 937-966, December.
    5. Yuan Wu & Christina D. Chambers & Ronghui Xu, 2019. "Semiparametric sieve maximum likelihood estimation under cure model with partly interval censored and left truncated data for application to spontaneous abortion," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 507-528, July.
    6. Liu, Xiaoyu & Xiang, Liming, 2021. "Generalized accelerated hazards mixture cure models with interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    7. Xiaoguang Wang & Ziwen Wang, 2021. "EM algorithm for the additive risk mixture cure model with interval-censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 91-130, January.
    8. Shuying Wang & Chunjie Wang & Jianguo Sun, 2021. "An additive hazards cure model with informative interval censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(2), pages 244-268, April.
    9. Bo Han & Ingrid Van Keilegom & Xiaoguang Wang, 2022. "Semiparametric estimation of the nonmixture cure model with auxiliary survival information," Biometrics, The International Biometric Society, vol. 78(2), pages 448-459, June.
    10. Li, Shuwei & Hu, Tao & Zhao, Xingqiu & Sun, Jianguo, 2019. "A class of semiparametric transformation cure models for interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 153-165.
    11. Han, Bo & Wang, Xiaoguang, 2020. "Semiparametric estimation for the non-mixture cure model in case-cohort and nested case-control studies," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    12. Ana Ezquerro & Brais Cancela & Ana López-Cheda, 2023. "On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility," Mathematics, MDPI, vol. 11(19), pages 1-21, October.

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