Likelihood-based methods for estimating the association between a health outcome and left- or interval-censored longitudinal exposure data.
Likelihood-based methods for estimating the association between a health outcome and left- or interval-censored longitudinal exposure data.
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基于可能性的方法，用于估计健康结果与左或区间删失纵向暴露数据之间的关联。
DOI：
10.1002/sim.3905
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发表时间：
2010
影响因子：
2
通讯作者：
Marcus,Michele
中科院分区：
文献类型：
--
作者：
Wannemuehler,KathleenA;Lyles,RobertH;Manatunga,AmitaK;Terrell,MetreciaL;Marcus,Michele
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.
