Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study

Dasgupta, Paramita, Cramb, Susanna M, Aitken, Joanne F, Turrell, Gavin and Baade, Peter 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, vol. 13, pp. 1-14, doi: 10.1186/1476-072X-13-36.

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Title Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study
Author(s) Dasgupta, Paramita
Cramb, Susanna M
Aitken, Joanne F
Turrell, GavinORCID iD for Turrell, Gavin orcid.org/0000-0002-3576-8744
Baade, Peter D
Journal name International journal of health geographics
Volume number 13
Article ID 36
Start page 1
End page 14
Total pages 14
Publisher BioMed Central
Place of publication London, Eng.
Publication date 2014-10-04
ISSN 1476-072X
Keyword(s) Bayesian
Multilevel
Colorectal cancer
Epidemiology
All-cause survival
Spatial
Science & Technology
Life Sciences & Biomedicine
Public, Environmental & Occupational Health
Summary 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.
Language eng
DOI 10.1186/1476-072X-13-36
Field of Research 1604 Human Geography
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2014, Dasgupta et al.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30117704

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