Evaluation of the performance of smoothing functions in generalized additive models for spatial variation in disease.

ABSTRACT: Generalized additive models (GAMs) with bivariate smoothing functions have been applied to estimate spatial variation in risk for many types of cancers. Only a handful of studies have evaluated the performance of smoothing functions applied in GAMs with regard to different geographical areas of elevated risk and different risk levels. This study evaluates the ability of different smoothing functions to detect overall spatial variation of risk and elevated risk in diverse geographical areas at various risk levels using a simulation study. We created five scenarios with different true risk area shapes (circle, triangle, linear) in a square study region. We applied four different smoothing functions in the GAMs, including two types of thin plate regression splines (TPRS) and two versions of l

SUBMITTER: Siangphoe U 

PROVIDER: S-EPMC4415687 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature