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

نویسندگان

چکیده

This research is motivated by a periodontal disease dataset that possesses certain special features. The consists of clustered current status time-to-event observations with large and varying cluster sizes, where the size associated outcome. Also, heavy censoring present in data even long follow-up time, suggesting presence cured subpopulation. In this paper, we propose computationally efficient marginal approach, namely cluster-weighted generalized estimating equation to analyze based on class semiparametric transformation cure models. parametric nonparametric components model are estimated using Bernstein-polynomial sieve maximum pseudo-likelihood approach. asymptotic properties proposed estimators studied. Simulation studies conducted evaluate performance scenarios different degree informative clustering within-cluster dependence. method applied motivating for illustration.

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ژورنال

عنوان ژورنال: Statistics in Medicine

سال: 2021

ISSN: ['0277-6715', '1097-0258']

DOI: https://doi.org/10.1002/sim.8910