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Indexed by:期刊论文

Date of Publication:2018-01-01

Journal:Biometrical journal. Biometrische Zeitschrift

Volume:60

Issue:4

Page Number:780-796

ISSN No.:1521-4036

Key Words:ES algorithm; generalized estimating equations; logistic regression model; proportional hazards model; sandwich variance estimation

Abstract:There is a great deal of recent interests in modeling right-censored clustered survival time data with a possible fraction of cured subjects who are nonsusceptible to the event of interest using marginal mixture cure models. In this paper, we consider a semiparametric marginal mixture cure model for such data and propose to extend an existing generalized estimating equation approach by a new unbiased estimating equation for the regression parameters in the latency part of the model. The large sample properties of the regression effect estimators in both incidence and the latency parts are established. The finite sample properties of the estimators are studied in simulation studies. The proposed method is illustrated with a bone marrow transplantation data and a tonsil cancer data. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.