A systematic review of Bayesian spatial–temporal models on cancer incidence and mortality

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.