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Title: Partially linear transformation cure models for interval-censored data
Authors: Hu, Tao
Xiang, Liming
Keywords: Cure rate
Interval censoring
Issue Date: 2014
Source: Hu, T., & Xiang, L. (2016). Partially linear transformation cure models for interval-censored data. Computational Statistics and Data Analysis, 93, 257-269.
Series/Report no.: Computational Statistics and Data Analysis
Abstract: There has been considerable progress in the development of semiparametric transformation models for regression analysis of time-to-event data. However, most of the current work focuses on right-censored data. Significantly less work has been done for interval-censored data, especially when the population contains a nonignorable cured subgroup. A broad and flexible class of semiparametric transformation cure models is proposed for analyzing interval-censored data in the presence of a cure fraction. The proposed modeling approach combines a logistic regression formulation for the probability of cure with a partially linear transformation model for event times of susceptible subjects. The estimation is achieved by using a spline-based sieve maximum likelihood method, which is computationally efficient and leads to estimators with appealing properties such as consistency, asymptotic normality and semiparametric efficiency. Furthermore, a goodness-of-fit test can be constructed for the proposed models based on the sieve likelihood ratio. Simulations and a real data analysis are provided for illustration of the methodology.
ISSN: 0167-9473
DOI: 10.1016/j.csda.2014.08.014
Rights: © 2014 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Computational Statistics and Data Analysis, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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