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Estimation of dependence between paired correlated failure times in the presence of covariate measurement error

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  • Malka Gorfine
  • Li Hsu
  • Ross L. Prentice

Abstract

Summary. In many biomedical studies, covariates are subject to measurement error. Although it is well known that the regression coefficients estimators can be substantially biased if the measurement error is not accommodated, there has been little study of the effect of covariate measurement error on the estimation of the dependence between bivariate failure times. We show that the dependence parameter estimator in the Clayton–Oakes model can be considerably biased if the measurement error in the covariate is not accommodated. In contrast with the typical bias towards the null for marginal regression coefficients, the dependence parameter can be biased in either direction. We introduce a bias reduction technique for the bivariate survival function in copula models while assuming an additive measurement error model and replicated measurement for the covariates, and we study the large and small sample properties of the dependence parameter estimator proposed.

Suggested Citation

  • Malka Gorfine & Li Hsu & Ross L. Prentice, 2003. "Estimation of dependence between paired correlated failure times in the presence of covariate measurement error," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(3), pages 643-661, August.
    • Handle: RePEc:bla:jorssb:v:65:y:2003:i:3:p:643-661
    DOI: 10.1111/1467-9868.00407
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    Cited by:

    1. Anna Gottard, 2007. "On the inclusion of bivariate marked point processes in graphical models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 66(3), pages 269-287, November.

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