Event History Analysis in Multivariate Longitudinal Data

Type Thesis

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Summary

This thesis studies event history analysis in multivariate longitudinal observational databases (LODs) and its application in postmarketing surveillance to identify and measure the relationship between events of health outcomes and drug exposures. The LODs contain repeated measurements on each individual whose healthcare information is recorded electronically. Novel statistical methods are being developed to handle challenging issues arising from the scale and complexity of postmarketing surveillance LODs. In particular, the self-controlled case series (SCCS) method has been developed with two major features (1) it only uses individuals with at least one event for analysis and inference and, (2) it uses each individual to be served as his/her own control, effectively requiring a person to switch treatments during the observation period. Although this method handles heterogeneity and bias, it does not take full advantage of the observational databases. In this connection, the SCCS method may lead to a substantial loss of efficiency.

Edition Statements

Publication Date:2021

Thesis Information

Degree:Ph.D.
University:Columbia University
Advisor/Supervisor:Ying, Zhiliang
Pages:98

Publication Statements

Language:eng
Publication Date:2021

Edition Identifiers

proquest:2584363283

Subjects

applied mathematics Correlated frailty Drug exposures Dynamic random effect eng HEALTH OUTCOMES medical records Multivariate proportional intensity mod… Observational databases PDF Postmarketing surveillance Statistics