Semiparametric efficient estimation for additive hazards regression with case II interval-censored survival data

Baihua He, Yanyan Liu (), Yuanshan Wu and Xingqiu Zhao ()
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Baihua He: Wuhan University
Yanyan Liu: Wuhan University
Yuanshan Wu: Zhongnan University of Economics and Law
Xingqiu Zhao: The Hong Kong Polytechnic University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2020, vol. 26, issue 4, No 4, 708-730

Abstract: Abstract Interval-censored data often arise naturally in medical, biological, and demographical studies. As a matter of routine, the Cox proportional hazards regression is employed to fit such censored data. The related work in the framework of additive hazards regression, which is always considered as a promising alternative, remains to be investigated. We propose a sieve maximum likelihood method for estimating regression parameters in the additive hazards regression with case II interval-censored data, which consists of right-, left- and interval-censored observations. We establish the consistency and the asymptotic normality of the proposed estimator and show that it attains the semiparametric efficiency bound. The finite-sample performance of the proposed method is assessed via comprehensive simulation studies, which is further illustrated by a real clinical example for patients with hemophilia.

Keywords: Survival analysis; Interval-censored data; Additive hazards; Sieve maximum likelihood estimator; Semiparametric efficiency bound; Empirical process (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10985-020-09496-z

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