Likelihood‐based methods for estimating the association between a health outcome and left‐ or interval‐censored longitudinal exposure data |
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Authors: | Kathleen A Wannemuehler Robert H Lyles Amita K Manatunga Metrecia L Terrell Michele Marcus |
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Institution: | 1. Division of Foodborne, Bacterial and Mycotic Diseases, National Center for Zoonotic, Vectorborne and Enteric Diseases, Centers for Disease Control and Prevention, Atlanta, GA, U.S.A.;2. Department of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, Atlanta, GA, U.S.A.;3. Department of Epidemiology, The Rollins School of Public Health of Emory University, Atlanta, GA, U.S.A.;4. Department of Environmental and Occupational Health, The Rollins School of Public Health of Emory University, Atlanta, GA, U.S.A. |
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Abstract: | The Michigan Female Health Study (MFHS) conducted research focusing on reproductive health outcomes among women exposed to polybrominated biphenyls (PBBs). In the work presented here, the available longitudinal serum PBB exposure measurements are used to obtain predictions of PBB exposure for specific time points of interest via random effects models. In a two‐stage approach, a prediction of the PBB exposure is obtained and then used in a second‐stage health outcome model. This paper illustrates how a unified approach, which links the exposure and outcome in a joint model, provides an efficient adjustment for covariate measurement error. We compare the use of empirical Bayes predictions in the two‐stage approach with results from a joint modeling approach, with and without an adjustment for left‐ and interval‐censored data. The unified approach with the adjustment for left‐ and interval‐censored data resulted in little bias and near‐nominal confidence interval coverage in both the logistic and linear model setting. Published in 2010 by John Wiley & Sons, Ltd. |
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Keywords: | detection limits censoring environmental epidemiology interval measurement error prediction random effects |
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