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The international journal of biostatistics
Mar 24, 2025;

Regression analysis of clustered current status data with informative cluster size under a transformed survival model.

Yanqin Feng, Shijiao Yin, Jieli Ding
Author Information
  1. Yanqin Feng: School of Mathematics and Statistics, 12390 Wuhan University , Wuhan, 430072, P.R. China.
  2. Shijiao Yin: School of Mathematics and Statistics, 12390 Wuhan University , Wuhan, 430072, P.R. China.
  3. Jieli Ding: School of Mathematics and Statistics, 12390 Wuhan University , Wuhan, 430072, P.R. China.
PMID: 40116374 DOI: 10.1515/ijb-2023-0130

Abstract

In this paper, we study inference methods for regression analysis of clustered current status data with informative cluster sizes. When the correlated failure times of interest arise from a general class of semiparametric transformation frailty models, we develop a nonparametric maximum likelihood estimation based method for regression analysis and conduct an expectation-maximization algorithm to implement it. The asymptotic properties including consistency and asymptotic normality of the proposed estimators are established. Extensive simulation studies are conducted and indicate that the proposed method works well. The developed approach is applied to analyze a real-life data set from a tumorigenicity study.

Keywords

clustered current status data expectation-maximization algorithm nonparametric maximum likelihood semiparametric transformation frailty models

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Journal Article

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Created with Highcharts 10.0.0dataanalysisclusteredcurrentstatusstudyregressioninformativeclustersemiparametrictransformationfrailtymodelsnonparametricmaximumlikelihoodmethodexpectation-maximizationalgorithmasymptoticproposedpaperinferencemethodssizescorrelatedfailuretimesinterestarisegeneralclassdevelopestimationbasedconductimplementpropertiesincludingconsistencynormalityestimatorsestablishedExtensivesimulationstudiesconductedindicateworkswelldevelopedapproachappliedanalyzereal-lifesettumorigenicityRegressionsizetransformedsurvivalmodel

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