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Non‐parametric Maximum Likelihood Estimation for Cox Regression with Subject‐Specific Measurement Error

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  • C. Y. WANG

Abstract

. Many epidemiological studies have been conducted to identify an association between nutrient consumption and chronic disease risk. To this problem, Cox regression with additive covariate measurement error has been well developed in the literature. However, researchers are concerned with the validity of the additive measurement error assumption for self‐report nutrient data. Recently, some study designs using more reliable biomarker data have been considered, in which the additive measurement error assumption is more likely to hold. Biomarker data are often available in a subcohort. Self‐report data often encounter with a variety of serious biases. Complications arise primarily because the magnitude of measurement errors is often associated with some characteristics of a study subject. A more general measurement error model has been developed for self‐report data. In this paper, a non‐parametric maximum likelihood (NPML) estimator using an EM algorithm is proposed to simultaneously adjust for the general measurement errors.

Suggested Citation

  • C. Y. Wang, 2008. "Non‐parametric Maximum Likelihood Estimation for Cox Regression with Subject‐Specific Measurement Error," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 613-628, December.
    • Handle: RePEc:bla:scjsta:v:35:y:2008:i:4:p:613-628
    DOI: 10.1111/j.1467-9469.2008.00605.x
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    References listed on IDEAS

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    1. Sharon X. Xie & C. Y. Wang & Ross L. Prentice, 2001. "A risk set calibration method for failure time regression by using a covariate reliability sample," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 855-870.
    2. Ross L. Prentice & Mary Pettinger & Garnet L. Anderson, 2005. "Statistical Issues Arising in the Women's Health Initiative," Biometrics, The International Biometric Society, vol. 61(4), pages 899-911, December.
    3. C. Y. Wang, 2000. "Weighted Normality-Based Estimator in Correcting Correlation Coefficient Estimation Between Incomplete Nutrient Measurements," Biometrics, The International Biometric Society, vol. 56(1), pages 106-112, March.
    4. Elizabeth A. Sugar & Ching-Yun Wang & Ross L. Prentice, 2007. "Logistic Regression with Exposure Biomarkers and Flexible Measurement Error," Biometrics, The International Biometric Society, vol. 63(1), pages 143-151, March.
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