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Regression analysis of multivariate current status data with auxiliary covariates under the additive hazards model

Author

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  • Chen, Yurong
  • Feng, Yanqin
  • Sun, Jianguo

Abstract

In a biomedical study, it often occurs that some covariates of interest are not measured exactly and only some auxiliary information on them is available. In this case, a question of interest is how to make use of the available auxiliary information for statistical analysis. This paper discusses this problem in the context of regression analysis of multivariate current status failure time data arising from the additive hazards model. More specifically, we consider the situation where the covariates of interest are assessed only for the subjects in a validation set and a continuous auxiliary covariate is available for all subjects. For the problem, by employing the marginal model approach, we propose two procedures for estimation of regression parameters. The methods can be easily implemented and the asymptotic properties of the resulting estimators are established. Also an extensive simulation study is conducted for the evaluation of the proposed methods and indicates that they work well in practice. An illustrative example is provided.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:87:y:2015:i:c:p:34-45
    DOI: 10.1016/j.csda.2015.01.005
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    References listed on IDEAS

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    1. Yanyan Liu & Haibo Zhou & Jianwen Cai, 2009. "Estimated Pseudopartial-Likelihood Method for Correlated Failure Time Data with Auxiliary Covariates," Biometrics, The International Biometric Society, vol. 65(4), pages 1184-1193, December.
    2. Chengcheng Hu & D. Y. Lin, 2002. "Cox Regression with Covariate Measurement Error," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(4), pages 637-655, December.
    3. 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.
    4. Liu, Yanyan & Wu, Yuanshan & Zhou, Haibo, 2010. "Multivariate failure times regression with a continuous auxiliary covariate," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 679-691, March.
    5. 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.
    6. Halbo Zhou & C.‐Y. Wang, 2000. "Failure time regression with continuous covariates measured with error," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 657-665.
    7. Xue H. & Lam K.F. & Li G., 2004. "Sieve Maximum Likelihood Estimator for Semiparametric Regression Models With Current Status Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 346-356, January.
    8. 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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