期刊
BIOSTATISTICS
卷 10, 期 2, 页码 335-351出版社
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxn041
关键词
Air pollution; Confounding; Data synthesis; Low birth weight; Multiple imputation
资金
- Economic and Social Research Council [RES-576-25-5003]
- Department of Health [PHI/03/C1/045]
- Medical Research Council [U. 1052.00.008, U. 1052.00.001]
- MRC [MC_U105232027] Funding Source: UKRI
- Economic and Social Research Council [RES-576-25-5003] Funding Source: researchfish
- Medical Research Council [MC_U105232027] Funding Source: researchfish
Routinely collected administrative data sets, such as national registers, aim to collect information on a limited number of variables for the whole population. In contrast, survey and cohort studies contain more detailed data from a sample of the population. This paper describes Bayesian graphical models for fitting a common regression model to a combination of data sets with different sets of covariates. The methods are applied to a study of low birth weight and air pollution in England and Wales using a combination of register, survey, and small-area aggregate data. We discuss issues such as multiple imputation of confounding variables missing in one data set, survey selection bias, and appropriate propagation of information between model components. From the register data, there appears to be an association between low birth weight and environmental exposure to NO(2), but after adjusting for confounding by ethnicity and maternal smoking by combining the register and survey data under our models, we find there is no significant association. However, NO(2) was associated with a small but significant reduction in birth weight, modeled as a continuous variable.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据