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Functional modeling of recurrent events on time‐to‐event processes
Author(s) -
Spreafico Marta,
Ieva Francesca
Publication year - 2021
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.202000374
Subject(s) - event (particle physics) , computer science , statistics , econometrics , mathematics , quantum mechanics , physics
In clinical practice, it is often the case where the association between the occurrence of events and time‐to‐event outcomes is of interest; thus, it can be modeled within the framework of recurrent events. The purpose of our study is to enrich the information available for modeling survival with relevant dynamic features, properly taking into account their possibly time‐varying nature, as well as to provide a new setting for quantifying the association between time‐varying processes and time‐to‐event outcomes. We propose an innovative methodology to model information carried out by time‐varying processes by means of functional data, modeling each time‐varying variable as the compensator of marked point process the recurrent events are supposed to derive from. By means of Functional Principal Component Analysis, a suitable dimensional reduction of these objects is carried out in order to plug them into a Cox‐type functional regression model for overall survival. We applied our methodology to data retrieved from the administrative databases of Lombardy Region (Italy), related to patients hospitalized for Heart Failure (HF) between 2000 and 2012. We focused on time‐varying processes of HF hospitalizations and multiple drugs consumption and we studied how they influence patients' overall survival. This novel way to account for time‐varying variables allowed to model self‐exciting behaviors, for which the occurrence of events in the past increases the probability of a new event, and to quantify the effect of personal behaviors and therapeutic patterns on survival, giving new insights into the direction of personalized treatment.