A data-driven approach for estimating the change-points and impact of major events on disease risk
Publication date: Available online 10 February 2019Source: Spatial and Spatio-temporal EpidemiologyAuthor(s): R Carroll, AB Lawson, S ZhaoAbstractConsidering 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.
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