Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study
Web of Science
AuthorDasgupta, P; Cramb, SM; Aitken, JF; Turrell, G; Baade, PD
Source TitleInternational Journal of Health Geographics
PublisherBIOMED CENTRAL LTD
University of Melbourne Author/sTurrell, Gavin
AffiliationMedicine Dentistry & Health Sciences
Document TypeJournal Article
CitationsDasgupta, P., Cramb, S. M., Aitken, J. F., Turrell, G. & Baade, P. D. (2014). Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study. INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS, 13 (1), https://doi.org/10.1186/1476-072X-13-36.
Access StatusOpen Access
BACKGROUND: Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Multilevel models assume independent geographical areas, whereas spatial models explicitly incorporate geographical correlation, often via a conditional autoregressive prior. However the relative merits of these methods for large population-based studies have not been explored. Using a case-study approach, we report on the implications of using multilevel and spatial survival models to study geographical inequalities in all-cause survival. METHODS: Multilevel discrete-time and Bayesian spatial survival models were used to study geographical inequalities in all-cause survival for a population-based colorectal cancer cohort of 22,727 cases aged 20-84 years diagnosed during 1997-2007 from Queensland, Australia. RESULTS: Both approaches were viable on this large dataset, and produced similar estimates of the fixed effects. After adding area-level covariates, the between-area variability in survival using multilevel discrete-time models was no longer significant. Spatial inequalities in survival were also markedly reduced after adjusting for aggregated area-level covariates. Only the multilevel approach however, provided an estimation of the contribution of geographical variation to the total variation in survival between individual patients. CONCLUSIONS: With little difference observed between the two approaches in the estimation of fixed effects, multilevel models should be favored if there is a clear hierarchical data structure and measuring the independent impact of individual- and area-level effects on survival differences is of primary interest. Bayesian spatial analyses may be preferred if spatial correlation between areas is important and if the priority is to assess small-area variations in survival and map spatial patterns. Both approaches can be readily fitted to geographically enabled survival data from international settings.
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