Binary Regression Analysis with Pooled Exposure Measurements: A Regression Calibration Approach
Article first published online: 21 JUL 2010
© 2010, The International Biometric Society No claim to original US government works
Volume 67, Issue 2, pages 636–645, June 2011
How to Cite
Zhang, Z. and Albert, P. S. (2011), Binary Regression Analysis with Pooled Exposure Measurements: A Regression Calibration Approach. Biometrics, 67: 636–645. doi: 10.1111/j.1541-0420.2010.01464.x
- Issue published online: 20 JUN 2011
- Article first published online: 21 JUL 2010
- Received November 2009. Revised March 2010. Accepted April 2010.
- Bias correction;
- Logistic regression;
- Measurement error;
- Missing covariates;
- Probit regression
Summary 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.