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A Class of Functional Methods for Error-Contaminated Survival Data Under Additive Hazards Models with Replicate Measurements

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  • Ying Yan
  • Grace Y. Yi

Abstract

Covariate measurement error has attracted extensive interest in survival analysis. Since Prentice, a large number of inference methods have been developed to handle error-prone data that are modulated with proportional hazards models. In contrast to proportional hazards models, additive hazards models offer a flexible tool to delineate survival processes. However, there is little research on measurement error effects under additive hazards models. In this article, we systematically investigate this important problem. New insights into measurement error effects are revealed, as opposed to well-documented results for proportional hazards models. In particular, we explore asymptotic bias of ignoring measurement error in the analysis. To correct for the induced bias, we develop a class of functional correction methods for measurement error effects. The validity of the proposed methods is carefully examined, and we investigate issues of model checking and model misspecification. Theoretical results are established, and are complemented with numerical assessments. Supplementary materials for this article are available online.

Suggested Citation

  • Ying Yan & Grace Y. Yi, 2016. "A Class of Functional Methods for Error-Contaminated Survival Data Under Additive Hazards Models with Replicate Measurements," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 684-695, April.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:514:p:684-695
    DOI: 10.1080/01621459.2015.1034317
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    References listed on IDEAS

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    1. Xiao Song & Yijian Huang, 2005. "On Corrected Score Approach for Proportional Hazards Model with Covariate Measurement Error," Biometrics, The International Biometric Society, vol. 61(3), pages 702-714, September.
    2. Hattori, Satoshi, 2006. "Some properties of misspecified additive hazards models," Statistics & Probability Letters, Elsevier, vol. 76(15), pages 1641-1646, September.
    3. Liuquan Sun & Zhigang Zhang & Jianguo Sun, 2006. "Additive hazards regression of failure time data with covariate measurement errors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 60(4), pages 497-509, November.
    4. Hu, Chengcheng & Lin, D.Y., 2004. "Semiparametric Failure Time Regression With Replicates of Mismeasured Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 105-118, January.
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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.
    3. Li-Pang Chen & Grace Y. Yi, 2021. "Semiparametric methods for left-truncated and right-censored survival data with covariate measurement error," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(3), pages 481-517, June.

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