Combining data from multiple sources, with applications to environmental risk assessment.
The classical statistical paradigm emphasizes the development and application of methods to account for sampling variability. Many modern day applications, however, require consideration of other sources of uncertainty that are not so easy to quantify. This paper presents a case study involving an assessment of the impact of in-utero methylmercury exposure on the Intelligence Quotient (IQ) of young children. We illustrate how familiar techniques such as hierarchical modeling, Bayesian methods and sensitivity analysis can be used to aid decision making in settings that involve substantial uncertainty.
Ryan
Louise
L
Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA. lryan@hsph.harvard.edu
D003627:Data Interpretation, Statistical; D003657:Decision Making; D004781:Environmental Exposure / Q000706:statistics & numerical data*; D015386:Hazardous Substances / Q000009:adverse effects*; D006801:Humans; D007360:Intelligence / Q000187:drug effects; D008767:Methylmercury Compounds / Q000009:adverse effects; D018570:Risk Assessment / Q000706:statistics & numerical data*; D035501:Uncertainty
D015386:Hazardous Substances; D008767:Methylmercury Compounds