Conditionally Specified Logistic Regression and Multivariate T-Link: Comparison of Two Methods of Modeling Data with Multiple Binary Responses

• Activity Number: 550
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Curtis Miller*+ and Johnnye Lewis and Gabriel Huerta and Glenn Stark and Chris Shuey and Miranda Cajero
• Companies: University of New Mexico and University of New Mexico and University of New Mexico and unknown and Southwest Research Information Center and University of New Mexico
• Keywords: Conditionally specified logistic regression ; multivariate t-link ; abandoned uranium mines ; Native American health

Abstract

Communities with exposure to environmental contaminants are frequently concerned about unidentified threats to their health.  It may be difficult to analyze and make risk statements about such exposures; Patterns of exposure and human behavior may be complex, and susceptibility will be affected by past and present health status of the community. We have collected a broad range of environmental, socio-economic, and health information from more than 1300 members of Navajo communities representing a range of exposures to uranium wastes at hundreds of abandoned mine sites. Kidney disease, hypertension, and diabetes were reported as binary variables. We would like to model these diseases together. The multivariate t-link, as described by O'Brien and Dunson (2004), is a standard method to model binary responses simultaneously. An alternative method, that differs in some fundamental ways, is conditionally specified logistic regression, introduced by Liu (1994). We apply both methods to the Navajo survey data. We discuss similarities and differences of results from the two modeling methods.