Abstract
Spatial models are becoming more popular in time-to-event data analysis. Commonly, the intrinsic conditional autoregressive prior is placed on an area level frailty term to allow for correlation between areas. We considered a range of Bayesian Weibull and Cox semiparametric spatial models to describe a dataset on hospitalisation of dengue. This paper aimed to extend these two models, to evaluate the suitability of these models for estimation and prediction of the length of stay, compare different spatial priors, and determine factors that significantly affect the duration of hospital stay for dengue fever patients in the case study location, namely Wahidin hospital in Makassar, Indonesia. We compared two different models with three different spatial priors with respect to goodness of fit and generalisability. For all models considered, the Leroux prior was preferred over the intrinsic conditional autoregressive and independent priors, but Cox and Weibull versions had similar predictive performance, model fit, and results. Age and platelet count were negatively associated with the length of stay, while red blood cell count was positively associated with the length of stay of dengue patients at this hospital. Using appropriate Bayesian spatial survival models enables identification of factors that substantively affect the length of stay.
Highlights
Time-to-event or survival analysis is a set of statistical procedures for analysing data for which the outcome variable is time until an event occurs
Bayesian spatial survival models have recently emerged in the literature, and commonly spatially structured priors are placed on area-specific frailty terms [3]
Most of the patients come from the Tamalanrea district followed by the Biringkanaya and Rappocini districts
Summary
Time-to-event or survival analysis is a set of statistical procedures for analysing data for which the outcome variable is time until an event occurs. The event is often death, recovery or disease incidence, and survival time is usually defined in days, weeks, or years. Data used in survival analyses are often collected over distinct spatial regions. Bayesian spatial survival models have recently emerged in the literature, and commonly spatially structured priors are placed on area-specific frailty terms [3]. An early example is the study of breast cancer and malignant melanoma patients using a fully Bayesian Cox model by incorporating spatial autocorrelation between neighbouring areas [4]. A Bayesian spatial survival model that included conditional autoregressive (CAR) distributed area-specific random effects (frailty)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Environmental Research and Public Health
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
