A data-driven approach for estimating the change-points and impact of major events on disease risk. (June 2019) - Carroll, R.; Lawson, A.B.; Zhao, S.
Highlights: It is important to consider major events in disease risk estimation. A spatio-temporal survival model has been developed to do this. An extension of this method furnished an automated data-driven approach for estimating the change-points for such events. These automated models lead to better goodness of fit and inference. Abstract: Considering the impact of events on disease risk is important. Here, a Bayesian spatio-temporal accelerated failure time model furnished an ideal situation for modeling events that could impact survival experience via spatial and temporal frailty estimates. Through a hierarchical structure, this model allowed the data to detect the change-point(s) in addition to generating the event-related estimates. Both a real data case study and a simulation study were employed for testing these methods. The results suggested that meaningful and accurate change-points could be detected. Further, accurate event-related estimates for individuals in relation to those change-points could be obtained. By allowing the data to drive the change-point choices, the models were better fitting and the inference was more accurate.