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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
Rights: ©2021 John Wiley & Sons, Ltd.
This is the peer reviewed version of the following article: Lam, KF, Lee, CY, Wong, KY, Bandyopadhyay, D. Marginal analysis of current status data with informative cluster size using a class of semiparametric transformation cure models. Statistics in Medicine. 2021; 40: 2400– 2412, which has been published in final form at This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
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