Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157028
Title: Generalized accelerated hazards mixture cure models with interval-censored data
Authors: Liu, Xiaoyu
Xiang, Liming
Keywords: Science::Mathematics
Issue Date: 2021
Source: Liu, X. & Xiang, L. (2021). Generalized accelerated hazards mixture cure models with interval-censored data. Computational Statistics and Data Analysis, 161, 107248-. https://dx.doi.org/10.1016/j.csda.2021.107248
Project: RG134/17 (S)
Journal: Computational Statistics and Data Analysis
Abstract: Existing semiparametric mixture cure models with interval-censored data often assume a survival model, such as the Cox proportional hazards model, proportional odds model, accelerated failure time model, or their transformations for the susceptible subjects. There are cases in practice that such conventional assumptions may be inappropriate for modeling survival outcomes of susceptible subjects. We propose a more flexible class of generalized accelerated hazards mixture cure models for analysis of interval-censored failure times in the presence of a cure fraction. We develop a sieve maximum likelihood estimation in which the unknown cumulative baseline hazard function is approximated by means of B-splines and bundled with regression parameters. The proposed estimator possesses the properties of consistency and asymptotic normality, and can achieve the optimal global convergence rate under some conditions. Simulation results demonstrate that the proposed estimator performs satisfactorily in finite samples. The application of the proposed method is illustrated by the analysis of smoking cessation data from a lung health study.
URI: https://hdl.handle.net/10356/157028
ISSN: 0167-9473
DOI: 10.1016/j.csda.2021.107248
Rights: © 2021 Elsevier B.V. All rights reserved. This paper was published in Computational Statistics and Data Analysis and is made available with permission of Elsevier B.V.
Fulltext Permission: embargo_20231007
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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