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Abstract Details
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Activity Number:
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634
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #302657 |
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Title:
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Regression Analysis with Exposure Status Assessed in Large Pools
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Author(s):
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Ji Lin*+ and Robert H. Lyles
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Companies:
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Emory University and Emory University
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Address:
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Rollins School of Public Health, Decatur, GA, 30030,
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Keywords:
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pooling ;
regression analysis ;
efficiency ;
em algorithm ;
maximum likelihood estimate
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Abstract:
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In a study of a large population with small prevalence of positive exposures, pooling specimens of individuals together and testing them as a batch can considerably increase efficiency and reduce laboratory cost. In this paper, we consider the case in which the presence or absence of an exposure is assessed for a batch of individuals whose specimens are pooled together. The regression relationship between the outcome and the actual exposure is of interest, although the actual exposure of each member of a pool is only indirectly observed through the pool-wise exposure. A previously proposed implementation of maximum likelihood provides a feasible solution; however, as the number of observations in a batch can sometimes be very large, direct maximization of the likelihood can be computationally tedious. We propose an EM algorithm for estimating parameters in generalized linear models with
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