A0423
Title: Generalized functional linear model with a point process predictor
Authors: Jiehuan Sun - University of Illinois at Chicago (United States) [presenting]
Kuang-Yao Lee - Temple University (United States)
Abstract: 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. A novel generalized functional linear regression model is proposed for a point process predictor. The proposed model is based on the joint modelling framework, where a log-Gaussian Cox process model is adopted for the point process predictor and a generalized linear regression model for the outcome. A new algorithm is also developed for fast model estimation based on the Gaussian variational approximation method. Extensive simulation studies are conducted to evaluate the performance of the proposed method and it is compared to competing methods. The performance of the 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.
