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A flexible parametric approach to examining spatial variation in relative survival
Author(s) -
Cramb Susanna M.,
Mengersen Kerrie L.,
Lambert Paul C.,
Ryan Louise M.,
Baade Peter D.
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7071
Subject(s) - variation (astronomy) , relative survival , parametric statistics , computer science , statistics , econometrics , survival analysis , mathematics , medicine , environmental health , population , cancer registry , physics , astrophysics
Most of the few published models used to obtain small‐area estimates of relative survival are based on a generalized linear model with piecewise constant hazards under a Bayesian formulation. Limitations of these models include the need to artificially split the time scale, restricted ability to include continuous covariates, and limited predictive capacity. Here, an alternative Bayesian approach is proposed: a spatial flexible parametric relative survival model. This overcomes previous limitations by combining the benefits of flexible parametric models: the smooth, well‐fitting baseline hazard functions and predictive ability, with the Bayesian benefits of robust and reliable small‐area estimates. Both spatially structured and unstructured frailty components are included. Spatial smoothing is conducted using the intrinsic conditional autoregressive prior. The model was applied to breast, colorectal, and lung cancer data from the Queensland Cancer Registry across 478 geographical areas. Advantages of this approach include the ease of including more realistic complexity, the feasibility of using individual‐level input data, and the capacity to conduct overall, cause‐specific, and relative survival analysis within the same framework. Spatial flexible parametric survival models have great potential for exploring small‐area survival inequalities, and we hope to stimulate further use of these models within wider contexts. Copyright © 2016 John Wiley & Sons, Ltd.