摘要：Heterogeneity, ranging from measurement error to variation among individuals or regions, influences all levels of data collected for risk assessment. In its role as a nemesis, heterogeneity can reduce the precision of estimates, change the shape of a population model, or reduce the generalizability of study results. In many contexts, however, heterogeneity is the primary object of inference. Indeed, some degree of heterogeneity in excess of a baseline amount associated with a statistical model is necessary in order to identify important determinants of response. This report outlines the causes and influences of heterogeneity, develops statistical methods used to estimate and account for it, discusses interpretations of heterogeneity, and shows how it should influence study design. Examples from dose-response modeling, identification of sensitive individuals, assessment of small area variations and meta analysis provide applied contexts. Full text Full text is available as a scanned copy of the original print version. Get a printable copy (PDF file) of the complete article (1.5M), or click on a page image below to browse page by page. Links to PubMed are also available for Selected References . 215 216 217 218 219 220 221 222
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