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

Abstract

BackgroundA 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 associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics.ResultsThis research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases.ConclusionsThe GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic.

Spatial Characterization of Hypertension Clusters using a Rural Australian Clinical Database

“…Another concern with SaTScan is the influence of the maximum window size [22]. GAM has been found to outperform SaTScan when clusters are irregularly shaped [23]. In addition, GAM returns a continuous surface of RR values, while SaTScan returns a single number within each cluster.…”

Evaluation of the Performance of Smoothing Functions in Generalized Additive Models for Spatial Variation in Disease

“…40,42 The performance of permutation testing for detecting significant spatial variation in risk with GAMs has been investigated in simulation studies. 21,31,41,43 Generally, the permutation procedure is based on Monte Carlo randomization of case labels and associated covariates, conditioning on the number and location of observed points. 44 Randomization of labels is consistent with the null hypothesis of constant risk throughout the study area.…”

A Spatial‐EWMA Framework for Detecting Clustering

“…Song and Kulldorff, 24 Kulldorff et al, 25 and Takahashi and Tango 26 compared the powers of detecting spatial clustering under different test statistics by using the simulated benchmark data. The comparisons of other LR-based spatial scan statistics were provided in Chan, 27 Young et al, 28 and Tsui et al 29 .…”