Generalized accelerated hazards mixture cure models with interval-censored data
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 con...
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sg-ntu-dr.10356-1570282023-02-28T20:06:33Z Generalized accelerated hazards mixture cure models with interval-censored data Liu, Xiaoyu Xiang, Liming School of Physical and Mathematical Sciences Science::Mathematics Bundled Regression Parameter Interval-Censoring 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. Ministry of Education (MOE) Submitted/Accepted version This research was supported by the Singapore Ministry of Education Academic Research Fund Tier 1 grant RG134/17 (S). 2022-04-30T08:06:52Z 2022-04-30T08:06:52Z 2021 Journal Article 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 0167-9473 https://hdl.handle.net/10356/157028 10.1016/j.csda.2021.107248 2-s2.0-85104063296 161 107248 en RG134/17 (S) Computational Statistics and Data Analysis © 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. application/pdf |
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Science::Mathematics Bundled Regression Parameter Interval-Censoring Liu, Xiaoyu Xiang, Liming Generalized accelerated hazards mixture cure models with interval-censored data |
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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. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Liu, Xiaoyu Xiang, Liming |
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Article |
author |
Liu, Xiaoyu Xiang, Liming |
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Liu, Xiaoyu |
title |
Generalized accelerated hazards mixture cure models with interval-censored data |
title_short |
Generalized accelerated hazards mixture cure models with interval-censored data |
title_full |
Generalized accelerated hazards mixture cure models with interval-censored data |
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Generalized accelerated hazards mixture cure models with interval-censored data |
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Generalized accelerated hazards mixture cure models with interval-censored data |
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generalized accelerated hazards mixture cure models with interval-censored data |
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2022 |
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https://hdl.handle.net/10356/157028 |
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