Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure

Dynamic clustering of hazard functions: an application to disease progression in chronic heart... We analyse data collected from the administrative datawarehouse of an Italian regional district (Lombardia) concerning patients affected by Chronic Heart Failure. The longitudinal data gathering for each patient hospital readmissions in time, as well as patient-specific covariates, is studied as a realization of non homogeneous Poisson process. Since the aim behind this study is to identify groups of patients behaving similarly in terms of disease progression and then healthcare consumption, we conjectured the time segments between two consecutive hospitalizations to be Weibull distributed in each hidden cluster. Adding a frailty term to take into account the within subjects unknown variability, the corresponding patient-specific hazard functions are reconstructed. Therefore, the comprehensive distribution for each time to event variable is modelled as a Weibull Mixture. We are then able to easily interpret the related hidden groups as healthy, sick, and terminally ill subjects. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Health Care Management Science Springer Journals

Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure

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Publisher
Springer US
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Business and Management; Operation Research/Decision Theory; Health Administration; Health Informatics; Management; Econometrics; Business and Management, general
ISSN
1386-9620
eISSN
1572-9389
D.O.I.
10.1007/s10729-016-9357-3
Publisher site
See Article on Publisher Site

Abstract

We analyse data collected from the administrative datawarehouse of an Italian regional district (Lombardia) concerning patients affected by Chronic Heart Failure. The longitudinal data gathering for each patient hospital readmissions in time, as well as patient-specific covariates, is studied as a realization of non homogeneous Poisson process. Since the aim behind this study is to identify groups of patients behaving similarly in terms of disease progression and then healthcare consumption, we conjectured the time segments between two consecutive hospitalizations to be Weibull distributed in each hidden cluster. Adding a frailty term to take into account the within subjects unknown variability, the corresponding patient-specific hazard functions are reconstructed. Therefore, the comprehensive distribution for each time to event variable is modelled as a Weibull Mixture. We are then able to easily interpret the related hidden groups as healthy, sick, and terminally ill subjects.

Journal

Health Care Management ScienceSpringer Journals

Published: Feb 4, 2016

References

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