Abstract #302139

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JSM 2003 Abstract #302139
Activity Number: 128
Type: Contributed
Date/Time: Monday, August 4, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #302139
Title: Sequential Estimation of Adjusted Attributable Risk in a Cross-Sectional Study: A Bootstrap Approach
Author(s): Charlotte C. Gard*+ and Abhik Das and Henrietta S. Bada and W. Kenneth Poole
Companies: Research Triangle Institute and Research Triangle Institute and University of Kentucky and Research Triangle Institute
Address: 6110 Executive Blvd., Suite 420, Rockville, MD, 20852,
Keywords: adjusted attributable risk ; sequential estimation ; bootstrap ; interval estimation ; cross-sectional design
Abstract:

The notion of attributable risk (AR) is popular in public health. This work was motivated by the Maternal Lifestyle Study, where risk factors associated with intrauterine growth restriction were identified in a multisite sample of newborns prenatally exposed to legal and illegal drugs. Of scientific interest was the etiologic fraction that can be eliminated should any of these exposures be prevented, while adjusting for a host of medical and sociodemographic covariates, including interactions. Aside from the large number of covariates, estimation of adjusted AR was further complicated since it was necessary to sequentially derive such estimates for several exposures, alone and in various combinations. Moreover, since existing techniques largely rely on complex algebraic manipulations even for one or two covariates (whereas this study had over 30), we adopt a bootstrap method to provide point and interval estimates for adjusted AR in cross-sectional studies. This approach is conceptually simple, avoiding the complex algebra that poses problems for large numbers of covariates, and can accommodate interactions as well as sequential estimation without added complications.

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