摘要：
This paper discusses issues faced in using epidemiologic data to develop
quantitative estimates of risk from specified patterns of exposure
to a toxicant. We focus on use of data from cohort studies with binary
endpoints (occurrence or non-occurrence of disease). Relative advantages
of Cox regression and Poisson regression are presented. A general
form of exposure metric is presented, and criteria for selecting an
appropriate metric are discussed. Advantages and disadvantages of
various dose-response models are discussed. It is argued that, unless
low-dose linearity of the dose response can be ruled out on non-statistical
grounds, then bounds for low-dose risk should incorporate low-dose
linearity; a sequential procedure for computing such bounds is illustrated.
Limitations in exposure data and their impact on risk assessments
are discussed. Issues arising when using meta-analytic techniques
to combine data from multiple epidemiologic studies are discussed.
Limitations in risk assessments resulting from reliance upon published
results alone are described. Methods for converting from measures
of risk used in epidemiologic studies (e.g., relative risk) to measures
appropriate for a risk assessment (e.g., additional lifetime probability
of disease occurrence resulting from a specific exposure pattern)
are described in detail. Several examples from the asbestos epidemiologic
literature are presented to illustrate these issues.
