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Inference in the additive risk model with time-varying covariates subject to measurement errors

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  • Sun, Liuquan
  • Zhou, Xian

Abstract

For the additive risk model with time-varying covariates which are subject to measurement errors, we study the estimation of both regression parameters and cumulative baseline hazard function. We first develop a procedure to estimate the regression parameters by correcting the bias of the naive estimator, and provide the large-sample properties of the bias-adjusted estimators. The procedure can be repeated to further improve the accuracy of the estimator. We then construct a corresponding estimator for the cumulative baseline hazard function and derive its asymptotic properties. Based on these results, confidence bands are constructed for the cumulative hazard function as well as the survival function. Monte Carlo studies are conducted to evaluate the performance of these estimators.

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  • Sun, Liuquan & Zhou, Xian, 2008. "Inference in the additive risk model with time-varying covariates subject to measurement errors," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2559-2566, November.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:16:p:2559-2566
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    Cited by:

    1. Yanlin Tang & Xinyuan Song & Grace Yun Yi, 2022. "Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(1), pages 139-168, January.
    2. Sandip Barui & Grace Y. Yi, 2020. "Semiparametric methods for survival data with measurement error under additive hazards cure rate models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 421-450, July.

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