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

53/2014

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

Thursday 13th November 2014

Ieva, F.; Paganoni, A.M., Pietrabissa, T.

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. 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. Adding a frailty term to take into account the unknown variability of each subject, the corresponding patient-specific hazard functions are reconstructed.

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Ieva, F., Paganoni, A.M., Pietrabissa, T. (2016) Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure. Health Care Management of Science. doi: 10.1007/s10729-016-9357-3