Alleviating Ecological Bias in Generalized Linear Models with Optimal Subsample Design

• Activity Number: 292
• Type: Contributed
• Date/Time: Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Adam N. Glynn*+ and Jon Wakefield and Mark S. Handcock and Thomas Richardson
• Companies: Harvard University and University of Washington and University of Washington and University of Washington
• Keywords: Ecological bias ; Combining information ; Within-area confounding ; Sampling

Abstract

In this paper, we illustrate that combining ecological data with subsample data in situations in which a generalized linear model (GLM) is appropriate provides two main benefits. First, by including the individual level subsample data, the biases associated with ecological inference in GLMs can be eliminated. Second, we can use readily available ecological data to design optimal subsampling schemes, so as to maximize information about parameters. We present applications of this methodology in sociology and epidemiology, showing that small, optimally chosen subsamples can be combined with ecological data to generate precise estimates relative to a simple random subsample.