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A Class of Weighted Estimating Equations for Semiparametric Transformation Models with Missing Covariates

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  • Yang Ning
  • Grace Yi
  • Nancy Reid

Abstract

In survival analysis, covariate measurements often contain missing observations; ignoring this feature can lead to invalid inference. We propose a class of weighted estimating equations for right†censored data with missing covariates under semiparametric transformation models. Time†specific and subject†specific weights are accommodated in the formulation of the weighted estimating equations. We establish unified results for estimating missingness probabilities that cover both parametric and non†parametric modelling schemes. To improve estimation efficiency, the weighted estimating equations are augmented by a new set of unbiased estimating equations. The resultant estimator has the so†called ‘double robustness’ property and is optimal within a class of consistent estimators.

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  • Yang Ning & Grace Yi & Nancy Reid, 2018. "A Class of Weighted Estimating Equations for Semiparametric Transformation Models with Missing Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(1), pages 87-109, March.
    • Handle: RePEc:bla:scjsta:v:45:y:2018:i:1:p:87-109
    DOI: 10.1111/sjos.12289
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    Cited by:

    1. Du, Mingyue & Li, Huiqiong & Sun, Jianguo, 2021. "Regression analysis of censored data with nonignorable missing covariates and application to Alzheimer Disease," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    2. 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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