Joint analysis of interval-censored failure time data and panel count data
Da Xu,
Hui Zhao and
Jianguo Sun ()
Additional contact information
Da Xu: Jilin University
Hui Zhao: Central China Normal University
Jianguo Sun: Jilin University
Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 1, No 6, 94-109
Abstract:
Abstract Interval-censored failure time data and panel count data are two types of incomplete data that commonly occur in event history studies and many methods have been developed for their analysis separately (Sun in The statistical analysis of interval-censored failure time data. Springer, New York, 2006; Sun and Zhao in The statistical analysis of panel count data. Springer, New York, 2013). Sometimes one may be interested in or need to conduct their joint analysis such as in the clinical trials with composite endpoints, for which it does not seem to exist an established approach in the literature. In this paper, a sieve maximum likelihood approach is developed for the joint analysis and in the proposed method, Bernstein polynomials are used to approximate unknown functions. The asymptotic properties of the resulting estimators are established and in particular, the proposed estimators of regression parameters are shown to be semiparametrically efficient. In addition, an extensive simulation study was conducted and the proposed method is applied to a set of real data arising from a skin cancer study.
Keywords: Bernstein polynomial; Event history study; Frailty model; Sieve maximum likelihood estimation (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10985-017-9397-0
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