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Efficient estimation in additive hazards regression with current status data

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  • Torben Martinussen

Abstract

Current status data arise when the exact timing of an event is unobserved, and it is only known at a given point in time whether or not the event has occurred. Recently Lin et al. (1998) studied the additive semiparametric hazards model for current status data. They showed that the analysis of current status data under the additive hazards model reduces to ordinary Cox regression under the assumption that a proportional hazards model may be used to describe the monitoring intensity. This analysis does not make efficient use of data, and in some cases it may not be appropriate to assume a proportional hazards model for the monitoring times. We study the semiparametric hazards model for current status data but make use of the semiparametric efficient score function. The suggested approach has the advantages that it is efficient in that it reaches the semiparametric information bound, and it does not involve any modelling of the monitoring times. Copyright Biometrika Trust 2002, Oxford University Press.

Suggested Citation

  • Torben Martinussen, 2002. "Efficient estimation in additive hazards regression with current status data," Biometrika, Biometrika Trust, vol. 89(3), pages 649-658, August.
    • Handle: RePEc:oup:biomet:v:89:y:2002:i:3:p:649-658
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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. Xuewen Lu & Peter X.-K. Song, 2015. "Efficient Estimation of the Partly Linear Additive Hazards Model with Current Status Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 306-328, March.
    3. Huazhen Yu & Rui Zhang & Lixin Zhang, 2024. "Copula-based analysis of dependent current status data with semiparametric linear transformation model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(4), pages 742-775, October.
    4. 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.
    5. 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.
    6. Junlong Li & Chunjie Wang & Jianguo Sun, 2012. "Regression analysis of clustered interval-censored failure time data with the additive hazards model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 1041-1050, December.
    7. Yanqin Feng & Ling Ma & Jianguo Sun, 2015. "Regression Analysis of Current Status Data Under the Additive Hazards Model with Auxiliary Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 118-136, March.
    8. Shuangge Ma, 2007. "Additive risk model with case-cohort sampled current status data," Statistical Papers, Springer, vol. 48(4), pages 595-608, October.
    9. Lu, Xuewen & Pordeli, Pooneh & Burke, Murray D. & Song, Peter X.-K., 2016. "Partially linear single-index proportional hazards model with current status data," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 14-36.
    10. Baihua He & Yanyan Liu & Yuanshan Wu & Xingqiu Zhao, 2020. "Semiparametric efficient estimation for additive hazards regression with case II interval-censored survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 708-730, October.
    11. 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.
    12. Li, Shuwei & Hu, Tao & Wang, Peijie & Sun, Jianguo, 2017. "Regression analysis of current status data in the presence of dependent censoring with applications to tumorigenicity experiments," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 75-86.
    13. Shanshan Lu & Jingjing Wu & Xuewen Lu, 2019. "Efficient estimation of the varying-coefficient partially linear proportional odds model with current status data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(2), pages 173-194, March.
    14. Debashis Ghosh, 2004. "Nonparametric and semiparametric inference for models of tumor size and metastasis," The University of Michigan Department of Biostatistics Working Paper Series 1035, Berkeley Electronic Press.
    15. Nils Lid Hjort & Emil Aas Stoltenberg, 2023. "The partly parametric and partly nonparametric additive risk model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 372-402, April.
    16. Shuangge Ma, 2011. "Additive risk model for current status data with a cured subgroup," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(1), pages 117-134, February.
    17. 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.
    18. 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.
    19. Zhiguo Li & Kouros Owzar, 2016. "Fitting Cox Models with Doubly Censored Data Using Spline-Based Sieve Marginal Likelihood," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 476-486, June.
    20. Wanrong Liu & Jianglin Fang & Xuewen Lu, 2018. "Additive–multiplicative hazards model with current status data," Computational Statistics, Springer, vol. 33(3), pages 1245-1266, September.
    21. Debashis Ghosh, 2003. "Goodness-of-Fit Methods for Additive-Risk Models in Tumorigenicity Experiments," Biometrics, The International Biometric Society, vol. 59(3), pages 721-726, September.
    22. 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.
    23. Lu, Xuewen & Song, Peter X.-K., 2012. "On efficient estimation in additive hazards regression with current status data," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2051-2058.
    24. Ramesh Gupta, 2016. "Properties of additive frailty model in survival analysis," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(1), pages 1-17, January.

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