Abstract
Missing covariate data arise frequently in biomedical studies. In this article, we propose a class of weighted estimating equations for the additive hazard regression model when some of the covariates are missing at random. Time-specific and subject-specific weights are incorporated into the formulation of weighted estimating equations. Unified results are established for estimating selection probabilities that cover both parametric and non-parametric modeling schemes. The resulting estimators have closed forms and are shown to be consistent and asymptotically normal. Simulation studies indicate that the proposed estimators perform well for practical settings. An application to a mouse leukemia study is illustrated.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 11771431, 11690015, 11926341, 11601080 and 11671275), Key Laboratory of Random Complex Structures and Data Science, Chinese Academy of Sciences (Grant No. 2008DP173182) and the Fundamental Research Funds for the Central Universities in University of International Business and Economics (Grant No. CXTD10-09). The authors thank the two reviewers for their constructive and insightful comments and suggestions that greatly improved the paper.
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Jin, J., Ye, P. & Sun, L. A class of weighted estimating equations for additive hazard models with covariates missing at random. Sci. China Math. 65, 583–602 (2022). https://doi.org/10.1007/s11425-019-1699-4
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DOI: https://doi.org/10.1007/s11425-019-1699-4