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A data-driven approach for estimating the change-points and impact of major events on disease risk.

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.

SUBMITTER: Carroll R

PROVIDER: S-EPMC7971716 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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A data-driven approach for estimating the change-points and impact of major events on disease risk.

Carroll R R Lawson A B AB Zhao S S

Spatial and spatio-temporal epidemiology 20190210

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 ...[more]

PMID: 31128619

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