Logistic regression with exposure biomarkers and flexible measurement error
Logistic regression with exposure biomarkers and flexible measurement error
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DOI：
10.1111/j.1541-0420.2006.00632.x
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发表时间：
2007-03-01
期刊：
影响因子：
1.9
通讯作者：
Prentice, Ross L.
中科院分区：
文献类型：
--
作者：
Sugar, Elizabeth A.;Wang, Ching-Yun;Prentice, Ross L.
回归校准，改进的回归校准，和条件评分估计程序扩展到一个测量模型，是出于营养和体力活动流行病学。由于成本原因，在研究队列的一小部分上可获得的生物标记数据被假定为遵守经典测量误差模型，而相应的自我报告的营养素消耗或活动相关的能量消耗数据可用于整个队列。自我报告评估测量模型包括个人特定的随机效应，其均值和方差可能取决于个体特征，如体重指数或种族。逻辑回归用于将疾病比值比与实际但未测量的饮食或身体活动暴露联系起来。模拟研究，以评估和对比三种估计程序，并提供深入了解在选定的队列研究配置下的首选生物标志物子样本量。
Regression calibration, refined regression calibration, and conditional scores estimation procedures are extended to a measurement model that is motivated by nutritional and physical activity epidemiology. Bioinarker data, available on a small subset of a study cohort for reasons of cost, are assumed to adhere to a classical measurement error model, while corresponding self-report nutrient consumption or activity-related energy expenditure data are available for the entire cohort. The self-report assessment measurement model includes a person-specific random effect, the mean and variance of which may depend on individual characteristics such as body mass index or ethnicity. Logistic regression is used to relate the disease odds ratio to the actual, but unmeasured, dietary or physical activity exposure. Simulation studies are presented to evaluate and contrast the three estimation procedures, and to provide insight into preferred biomarker subsample size under selected cohort study configurations.