Win Wah1, Susannah Ahern2, Arul Earnest2. 1. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia. Win.Wah@monash.edu. 2. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Abstract
OBJECTIVES: This study aimed to review the types and applications of fully Bayesian (FB) spatial-temporal models and covariates used to study cancer incidence and mortality. METHODS: This systematic review searched articles published within Medline, Embase, Web-of-Science and Google Scholar between 2014 and 2018. RESULTS: A total of 38 studies were included in our study. All studies applied Bayesian spatial-temporal models to explore spatial patterns over time, and over half assessed the association with risk factors. Studies used different modelling approaches and prior distributions for spatial, temporal and spatial-temporal interaction effects depending on the nature of data, outcomes and applications. The most common Bayesian spatial-temporal model was a generalized linear mixed model. These models adjusted for covariates at the patient, area or temporal level, and through standardization. CONCLUSIONS: Few studies (4) modelled patient-level clinical characteristics (11%), and the applications of an FB approach in the forecasting of spatial-temporally aligned cancer data were limited. This review highlighted the need for Bayesian spatial-temporal models to incorporate patient-level prognostic characteristics through the multi-level framework and forecast future cancer incidence and outcomes for cancer prevention and control strategies.
OBJECTIVES: This study aimed to review the types and applications of fully Bayesian (FB) spatial-temporal models and covariates used to study cancer incidence and mortality. METHODS: This systematic review searched articles published within Medline, Embase, Web-of-Science and Google Scholar between 2014 and 2018. RESULTS: A total of 38 studies were included in our study. All studies applied Bayesian spatial-temporal models to explore spatial patterns over time, and over half assessed the association with risk factors. Studies used different modelling approaches and prior distributions for spatial, temporal and spatial-temporal interaction effects depending on the nature of data, outcomes and applications. The most common Bayesian spatial-temporal model was a generalized linear mixed model. These models adjusted for covariates at the patient, area or temporal level, and through standardization. CONCLUSIONS: Few studies (4) modelled patient-level clinical characteristics (11%), and the applications of an FB approach in the forecasting of spatial-temporally aligned cancer data were limited. This review highlighted the need for Bayesian spatial-temporal models to incorporate patient-level prognostic characteristics through the multi-level framework and forecast future cancer incidence and outcomes for cancer prevention and control strategies.