Generalized functional linear model with a point process predictor.
generalized functional linear model
joint modeling
point process data
variational approximation
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
15 Apr 2024
15 Apr 2024
Historique:
revised:
17
12
2023
received:
03
09
2023
accepted:
15
01
2024
medline:
18
3
2024
pubmed:
9
2
2024
entrez:
9
2
2024
Statut:
ppublish
Résumé
Point process data have become increasingly popular these days. For example, many of the data captured in electronic health records (EHR) are in the format of point process data. It is of great interest to study the association between a point process predictor and a scalar response using generalized functional linear regression models. Various generalized functional linear regression models have been developed under different settings in the past decades. However, existing methods can only deal with functional or longitudinal predictors, not point process predictors. In this article, we propose a novel generalized functional linear regression model for a point process predictor. Our proposed model is based on the joint modeling framework, where we adopt a log-Gaussian Cox process model for the point process predictor and a generalized linear regression model for the outcome. We also develop a new algorithm for fast model estimation based on the Gaussian variational approximation method. We conduct extensive simulation studies to evaluate the performance of our proposed method and compare it to competing methods. The performance of our proposed method is further demonstrated on an EHR dataset of patients admitted into the intensive care units of the Beth Israel Deaconess Medical Center between 2001 and 2008.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1564-1576Informations de copyright
© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
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