Estimation of the additive hazards model with interval-censored data and missing covariates |
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| Authors: | Huiqiong Li Han Zhang Liang Zhu Ni Li Jianguo Sun |
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| Affiliation: | 1. Department of-10 Statistics, Yunnan University, Kunming, 650091 P.R. China;2. Department of Statistics, University of Missouri, Columbia, MO, 65211 U.S.A;3. Biostatistics and Epidemiology Research Design, Health Science Center at Houston, University of Texas, Houston, TX, U.S.A;4. School of Mathematics and Statistics, Hainan Normal University, Haikou, 571158 P.R. China |
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| Abstract: | The additive hazards model is one of the most commonly used regression models in the analysis of failure time data and many methods have been developed for its inference in various situations. However, no established estimation procedure exists when there are covariates with missing values and the observed responses are interval-censored; both types of complications arise in various settings including demographic, epidemiological, financial, medical and sociological studies. To address this deficiency, we propose several inverse probability weight-based and reweighting-based estimation procedures for the situation where covariate values are missing at random. The resulting estimators of regression model parameters are shown to be consistent and asymptotically normal. The numerical results that we report from a simulation study suggest that the proposed methods work well in practical situations. An application to a childhood cancer survival study is provided. The Canadian Journal of Statistics 48: 499–517; 2020 © 2020 Statistical Society of Canada |
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| Keywords: | Additive hazards model interval-censored data inverse probability weighting missing covariates |
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