Modeling Heterogeneity in Healthcare Utilization Using Massive Medical Claims Data

DOI10.1080/01621459.2017.1330203zbMath1398.62341OpenAlexW2708768734WikidataQ57193278 ScholiaQ57193278MaRDI QIDQ4690932

Nicoleta Serban, Ross P. Hilton, Yuchen Zheng

Publication date: 23 October 2018

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6167939

zbMATH Keywords

survival analysisproportional hazards modellatent variable modelhealthcare utilizationmedicaid systempediatric asthma

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Fuzziness, and survival analysis and censored data (62N86)

Cites Work

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• Cox's regression model for counting processes: A large sample study
• A two-component Weibull mixture to model early and late mortality in a Bayesian framework
• Homogeneity testing in a Weibull mixture model
• Nonparametric inference for a family of counting processes
• Spatial accessibility of pediatric primary healthcare: measurement and inference
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Heterogeneity in dynamic discrete choice models
• Theory & Methods: Testing for Homogeneity in an Exponential Mixture Model
• Estimation in a Cox Proportional Hazards Cure Model
• Mixture models in econometric duration analysis
• A mixture model combining logistic regression with proportional hazards regression

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