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Robustness of estimation methods in a survival cure model with mismeasured covariates

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  • Bertrand, A.
  • Legrand, C.
  • Léonard, D.
  • Van Keilegom, I.

Abstract

In medical applications, time-to-event data is frequently encountered. While classical survival methods are well known and broadly used to analyze such data, they do not take into account two phenomena which appear quite often in practice: the presence of individuals who will never experience the event of interest (they are cured from this event) and of measurement error in the continuous covariates. Two approaches exist in the literature to estimate a model, taking these features into account. However, they require information about the distribution of the measurement error which is rarely fully known in practice. A theoretical study of bias motivates the need to take the measurement error into account. The conclusions of an extensive simulation study investigating the robustness of both correction approaches with respect to their assumptions then provide some practical recommendations for similar situations. Finally, the time until recurrence after surgery for rectal cancer patients is analyzed, taking into account the results from the simulations. Both correction methods were implemented in the R package miCoPTCM.

Suggested Citation

  • Bertrand, A. & Legrand, C. & Léonard, D. & Van Keilegom, I., 2017. "Robustness of estimation methods in a survival cure model with mismeasured covariates," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 3-18.
    • Handle: RePEc:eee:csdana:v:113:y:2017:i:c:p:3-18
    DOI: 10.1016/j.csda.2016.11.013
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    References listed on IDEAS

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    1. Luis E. Nieto‐Barajas & Guosheng Yin, 2008. "Bayesian Semiparametric Cure Rate Model with an Unknown Threshold," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 540-556, September.
    2. 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.
    3. Aurelie Bertrand & Catherine Legrand & Raymond J. Carroll & Christophe de Meester & Ingrid Van Keilegom, 2017. "Inference in a survival cure model with mismeasured covariates using a simulation-extrapolation approach," Biometrika, Biometrika Trust, vol. 104(1), pages 31-50.
    4. Bertrand, Aurelie & Legrand, Catherine & Carroll, Raymond J. & de Meester de Ravenstein, Christophe & Van Keilegom, Ingrid, 2017. "Inference in a survival cure model with mismeasured covariates using a simulation-extrapolation approach," LIDAM Reprints ISBA 2017046, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Ma, Yanyuan & Yin, Guosheng, 2008. "Cure Rate Model With Mismeasured Covariates Under Transformation," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 743-756, June.
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    Cited by:

    1. Bertrand, Aurelie & Van Keilegom, Ingrid & Legrand, Catherine, 2017. "Flexible parametric approach to classical measurement error variance estimation without auxiliary data," LIDAM Discussion Papers ISBA 2017025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Amico, Mailis & Van Keilegom, Ingrid, 2017. "Cure models in survival analysis," LIDAM Discussion Papers ISBA 2017007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Li, Mengyan & Ma, Yanyuan & Li, Runze, 2019. "Semiparametric regression for measurement error model with heteroscedastic error," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 320-338.
    4. Aurélie Bertrand & Ingrid Van Keilegom & Catherine Legrand, 2019. "Flexible parametric approach to classical measurement error variance estimation without auxiliary data," Biometrics, The International Biometric Society, vol. 75(1), pages 297-307, March.
    5. 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).

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