Marginal analysis of current status data with informative cluster size using a class of semiparametric transformation cure models

Kwok Fai Lam, Chun Yin Lee, Kin Yau Wong, Dipankar Bandyopadhyay

Research output: Journal article publicationJournal articleAcademic researchpeer-review


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.

Original languageEnglish
Pages (from-to)2400-2412
Number of pages13
JournalStatistics in Medicine
Issue number10
Publication statusPublished - 10 May 2021


  • cure model
  • current status data
  • estimating equations
  • informative cluster size
  • survival analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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