ArticleInternational journal of health geographics2014
Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study.
Article in International journal of health geographics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Patient- and Areal-Level Risk Factors Associated With Lung Cancer Mortality in Victoria, Australia: A Bayesian Spatial Survival Analysis.Cancer medicine · 2024Article
- Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study.Malaria journal · 2022Article
- Cross-sectional study to predict subnational levels of health workers' knowledge about severe malaria treatment in Kenya.BMJ open · 2022Article
- Forecasting of Lung Cancer Incident Cases at the Small-Area Level in Victoria, Australia.International journal of environmental research and public health · 2021Article
- Objectively Measured Built Environments and Cardiovascular Diseases in Middle-Aged and Older Korean Adults.International journal of environmental research and public health · 2021Article
- Augmenting disease maps: a Bayesian meta-analysis approach.Royal Society open science · 2020Article
- Influence of Neighborhood Environment on Korean Adult Obesity Using a Bayesian Spatial Multilevel Model.International journal of environmental research and public health · 2019Article
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- Spatially varying effects of predictors for the survival prediction of nonmetastatic colorectal Cancer.BMC cancer · 2018Article
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5 authors.
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Abstract
backgroundMultilevel 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.
methodsMultilevel 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.
resultsBoth 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.
conclusionsWith 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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