Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90540
Title: Marginal analysis of current status data with informative cluster size using a class of semiparametric transformation cure models
Authors: Lam, KF
Lee, CY 
Wong, KY 
Bandyopadhyay, D
Issue Date: 10-May-2021
Source: Statistics in medicine, 10 May 2021, v. 40, no. 10, p. 2400-2412
Abstract: This research is motivated by a periodontal disease dataset that possesses certain special features. The dataset consists of clustered current status time-to-event observations with large and varying cluster sizes, where the cluster size is associated with the disease outcome. Also, heavy censoring is present in the data even with long follow-up time, suggesting the presence of a cured subpopulation. In this paper, we propose a computationally efficient marginal approach, namely the cluster-weighted generalized estimating equation approach, to analyze the data based on a class of semiparametric transformation cure models. The parametric and nonparametric components of the model are estimated using a Bernstein-polynomial based sieve maximum pseudo-likelihood approach. The asymptotic properties of the proposed estimators are studied. Simulation studies are conducted to evaluate the performance of the proposed estimators in scenarios with different degree of informative clustering and within-cluster dependence. The proposed method is applied to the motivating periodontal disease data for illustration.
Keywords: Cure model
Current status data
Estimating equations
Informative cluster size
Survival analysis
Publisher: John Wiley & Sons
Journal: Statistics in medicine 
ISSN: 0277-6715
EISSN: 1097-0258
DOI: 10.1002/sim.8910
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