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Estimation of the additive hazards model with interval‐censored data and missing covariates
Author(s) -
Li Huiqiong,
Zhang Han,
Zhu Liang,
Li Ni,
Sun Jianguo
Publication year - 2020
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11544
Subject(s) - covariate , missing data , statistics , estimator , econometrics , proportional hazards model , estimation , regression analysis , confidence interval , inference , mathematics , statistical inference , computer science , economics , artificial intelligence , management
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