Abstract:
:It has become increasingly common in epidemiological studies to pool specimens across subjects to achieve accurate quantitation of biomarkers and certain environmental chemicals. In this article, we consider the problem of fitting a binary regression model when an important exposure is subject to pooling. We take a regression calibration approach and derive several methods, including plug-in methods that use a pooled measurement and other covariate information to predict the exposure level of an individual subject, and normality-based methods that make further adjustments by assuming normality of calibration errors. Within each class we propose two ways to perform the calibration (covariate augmentation and imputation). These methods are shown in simulation experiments to effectively reduce the bias associated with the naive method that simply substitutes a pooled measurement for all individual measurements in the pool. In particular, the normality-based imputation method performs reasonably well in a variety of settings, even under skewed distributions of calibration errors. The methods are illustrated using data from the Collaborative Perinatal Project.
journal_name
Biometricsjournal_title
Biometricsauthors
Zhang Z,Albert PSdoi
10.1111/j.1541-0420.2010.01464.xsubject
Has Abstractpub_date
2011-06-01 00:00:00pages
636-45issue
2eissn
0006-341Xissn
1541-0420pii
BIOM1464journal_volume
67pub_type
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