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dc.contributor.authorLiu, Xiaoyuen_US
dc.contributor.authorXiang, Limingen_US
dc.identifier.citationLiu, X. & Xiang, L. (2021). Generalized accelerated hazards mixture cure models with interval-censored data. Computational Statistics and Data Analysis, 161, 107248-.
dc.description.abstractExisting 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.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.relationRG134/17 (S)en_US
dc.relation.ispartofComputational Statistics and Data Analysisen_US
dc.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.en_US
dc.titleGeneralized accelerated hazards mixture cure models with interval-censored dataen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.description.versionSubmitted/Accepted versionen_US
dc.subject.keywordsBundled Regression Parameteren_US
dc.description.acknowledgementThis research was supported by the Singapore Ministry of Education Academic Research Fund Tier 1 grant RG134/17 (S).en_US
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