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.Electronic supplementary materialThe online version of this article (doi:10.1186/1476-072X-13-36) contains supplementary material, which is available to authorized users.
Highlights
Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival
Geographic remoteness at colorectal cancer (CRC) diagnosis was classified according to the 2006 Australian Standard Geographical Classification Remoteness Index [26] and area-level disadvantage measured by the Index of Relative Socioeconomic Advantage and Disadvantage [27]
Study population The final cohort had a median age at diagnosis of 68 years and median follow-up time of 5.0 years with unadjusted 5-year all-cause survival of 58.1% (Table 3)
Summary
Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Much of the interest in the impact of area-level effects on cancer outcomes has been driven by the emergence of statistical methods that are designed to model geographically-structured data, including multilevel discrete-time [7,8] and more recently, Bayesian spatial [4,5] survival models. Multilevel discrete-time survival models [7,8] are designed to account for the nested structure of individuals within geographical areas They allow the simultaneous estimation of individual and area-level effects by modelling complex sources of variation at different hierarchical levels [9,10]. Observations from one geographical area are assumed to be statistically independent of those in another area, so any spatial associations between geographical areas are ignored [11]
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