Partially linear transformation cure models for interval-censored data

Tao Hu and Liming Xiang

Computational Statistics & Data Analysis, 2016, vol. 93, issue C, 257-269

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.

Keywords: Cure rate; Interval censoring; Semiparametric efficiency; Sieve likelihood ratio test; Sieve maximum likelihood estimator; Transformation (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:93:y:2016:i:c:p:257-269

DOI: 10.1016/j.csda.2014.08.014

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