期刊
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY
卷 172, 期 -, 页码 615-637出版社
WILEY
DOI: 10.1111/j.1467-985X.2008.00582.x
关键词
Adverse birth outcomes; Bayesian graphical model; Multiple biases; Sensitivity analysis; Water disinfection by-product
资金
- Imperial College BIAS [RES-576-25-5003]
- Economic and Social Research Council [RES-576-25-5003] Funding Source: researchfish
- Medical Research Council [G0801056B] Funding Source: researchfish
Data in the social, behavioural and health sciences frequently come from observational studies instead of controlled experiments. In addition to random errors, observational data typically contain additional sources of uncertainty such as missing values, unmeasured confounders and selection biases. Also, the research question is often different from that which a particular source of data was designed to answer, and so not all relevant variables are measured. As a result, multiple sources of data are often necessary to identify the biases and to inform about different aspects of the research question. Bayesian graphical models provide a coherent way to connect a series of local submodels, based on different data sets, into a global unified analysis. We present a unified modelling framework that will account for multiple biases simultaneously and give more accurate parameter estimates than standard approaches. We illustrate our approach by analysing data from a study of water disinfection by-products and adverse birth outcomes in the UK.
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