A power comparison of generalized additive models and the spatial scan statistic in a case-control setting.

ABSTRACT:

Background

A common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is

SUBMITTER: Young RL 

PROVIDER: S-EPMC2918545 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature