Wah W, Ahern S, Earnest A. A systematic review of Bayesian spatial-temporal models on cancer incidence and mortality.
Int J Public Health 2020;
65:673-682. [PMID:
32449006 DOI:
10.1007/s00038-020-01384-5]
[Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 04/26/2020] [Accepted: 05/02/2020] [Indexed: 12/12/2022] Open
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.
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