Challenges Associated with Integrating Data from Multiple Scales to Assess Relationships
Linda J. Young1, Carol A. Gotway2, Kenneth K. Lopiano1
1.Department of Statistics; IFAS, University of Florida, Gainesville, FL, USA
2.Epidemiology and Analysis Program Office, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
1.LJYoung@ufl.edu; KLopiano@ufl.edu; 2.Cdg7@cdc.gov
Abstract: Existing data from multiple sources (e.g.,surveillance systems, health registries, governmental agencies) are used increasingly in programs and studies for analysis and inference. More often than not, the data have been collected on different geographical or spatial units, and each of these may be different from the ones of interest. Rarely are investigators satisfied with combining the data on a common scale. After linking the variables, the focus naturally turns to exploring the relationships among the linked variables. Regression of the health outcomes on environmental factors, adjusted for appropriate covariates, is commonly used to quantify such associations. The effect of change-of-support is considered in this setting. Efforts to quantify the association between myocardial infarction (MI) and ozone within Florida illustrate some of the challenges.
Keywords: change-of-support; Berkson error; classical measurement error; spatial misalignment