A new approach to estimation of the proportional hazards model based on interval-censored data with missing covariates

Ruiwen Zhou, Huiqiong Li (), Jianguo Sun and Niansheng Tang
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Ruiwen Zhou: University of Missouri
Huiqiong Li: Yunnan University
Jianguo Sun: University of Missouri
Niansheng Tang: Yunnan University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2022, vol. 28, issue 3, No 1, 335-355

Abstract: Abstract This paper discusses the fitting of the proportional hazards model to interval-censored failure time data with missing covariates. Many authors have discussed the problem when complete covariate information is available or the missing is completely at random. In contrast to this, we will focus on the situation where the missing is at random. For the problem, a sieve maximum likelihood estimation approach is proposed with the use of I-spline functions to approximate the unknown cumulative baseline hazard function in the model. For the implementation of the proposed method, we develop an EM algorithm based on a two-stage data augmentation. Furthermore, we show that the proposed estimators of regression parameters are consistent and asymptotically normal. The proposed approach is then applied to a set of the data concerning Alzheimer Disease that motivated this study.

Keywords: Case II interval-censored data; EM algorithm; Missing at random; Sieve approach (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10985-022-09550-y

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