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Virginia Tech

1. Chen, Chen. Evaluating Time-varying Effect in Single-type and Multi-type Semi-parametric Recurrent Event Models.

Degree: PhD, Statistics, 2015, Virginia Tech

This dissertation aims to develop statistical methodologies for estimating the effects of time-fixed and time-varying factors in recurrent events modeling context. The research is motivated by the traffic safety research question of evaluating the influence of crash on driving risk and driver behavior. The methodologies developed, however, are general and can be applied to other fields. Four alternative approaches based on various data settings are elaborated and applied to 100-Car Naturalistic Driving Study in the following Chapters. Chapter 1 provides a general introduction and background of each method, with a sketch of 100-Car Naturalistic Driving Study. In Chapter 2, I assessed the impact of crash on driving behavior by comparing the frequency of distraction events in per-defined windows. A count-based approach based on mixed-effect binomial regression models was used. In Chapter 3, I introduced intensity-based recurrent event models by treating number of Safety Critical Incidents and Near Crash over time as a counting process. Recurrent event models fit the natural generation scheme of the data in this study. Four semi-parametric models are explored: Andersen-Gill model, Andersen-Gill model with stratified baseline functions, frailty model, and frailty model with stratified baseline functions. I derived model estimation procedure and and conducted model comparison via simulation and application. The recurrent event models in Chapter 3 are all based on proportional assumption, where effects are constant. However, the change of effects over time is often of primary interest. In Chapter 4, I developed time-varying coefficient model using penalized B-spline function to approximate varying coefficients. Shared frailty terms was used to incorporate correlation within subjects. Inference and statistical test are also provided. Frailty representation was proposed to link time-varying coefficient model with regular frailty model. In Chapter 5, I further extended framework to accommodate multi-type recurrent events with time-varying coefficient. Two types of recurrent-event models were developed. These models incorporate correlation among intensity functions from different type of events by correlated frailty terms. Chapter 6 gives a general review on the contributions of this dissertation and discussion of future research directions. Advisors/Committee Members: Guo, Feng (committeechair), Du, Pang (committee member), Hong, Yili (committee member), Reynolds, Marion R. (committee member).

Subjects/Keywords: Frailty Model; Generalized Linear Mixed Model; Multi-type Recurrent Event; Naturalistic Driving Study; Penalized B-Spline; Proportional Intensity Function; Stratification; Time-varying Coefficient; Transportation Safety

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chen, C. (2015). Evaluating Time-varying Effect in Single-type and Multi-type Semi-parametric Recurrent Event Models. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64371

Chicago Manual of Style (16th Edition):

Chen, Chen. “Evaluating Time-varying Effect in Single-type and Multi-type Semi-parametric Recurrent Event Models.” 2015. Doctoral Dissertation, Virginia Tech. Accessed July 17, 2019. http://hdl.handle.net/10919/64371.

MLA Handbook (7th Edition):

Chen, Chen. “Evaluating Time-varying Effect in Single-type and Multi-type Semi-parametric Recurrent Event Models.” 2015. Web. 17 Jul 2019.

Vancouver:

Chen C. Evaluating Time-varying Effect in Single-type and Multi-type Semi-parametric Recurrent Event Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/10919/64371.

Council of Science Editors:

Chen C. Evaluating Time-varying Effect in Single-type and Multi-type Semi-parametric Recurrent Event Models. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/64371

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