Predictive model algorithms identifying early and advanced stage ER+/HER2− breast cancer in claims data

Purpose Claims databases offer large populations for research, but lack clinical details. We aimed to develop predictive models to identify estrogen receptor positive (ER+) and human epidermal growth factor negative (HER2−) early breast cancer (ESBC) and advanced stage breast cancer (ASBC) in a clai...

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Bibliographic Details
Published in:	Pharmacoepidemiology and drug safety Vol. 28; no. 2; pp. 171 - 178
Main Authors:	Beachler, Daniel C., Luise, Cynthia, Yin, Ruihua, Gangemi, Kelsey, Cochetti, Philip T., Lanes, Stephan
Format:	Journal Article
Language:	English
Published:	England Wiley Subscription Services, Inc 01-02-2019
Subjects:	Administrative Claims, Healthcare - statistics & numerical data Adult Aged Algorithms Breast - pathology Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - epidemiology Breast Neoplasms - pathology claims Databases, Factual - statistics & numerical data Epidermal growth factor ErbB-2 protein Estrogen receptors Female HIRD Humans lasso regression Middle Aged Models, Biological molecular subtype Neoplasm Staging pharmacoepidemiology Prediction models predictive modeling Predictive Value of Tests Receptor, ErbB-2 - metabolism Receptors, Estrogen - metabolism Retrospective Studies Risk Assessment - methods stage Statistical analysis United States - epidemiology validation United States HIRD stage predictive modeling molecular subtype claims breast cancer lasso regression validation pharmacoepidemiology
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