fects of environmental agents were described in the report of the Health Effects Institute Environmental Epidemiology Planning Project (1993). Exposure assessment for studies of environmental agents was addressed in the first volume of this report. Thomas et al. (1993) have considered advances in contending with errors in exposure measures and their consequences.

Introduction

The most usual epidemiologic measure of effect of an environmental agent, the relative risk, is the ratio of the incidence of disease in those exposed to the agent of interest to the incidence in those not exposed. Categorization of subjects by exposure is straightforward in some types of epidemiologic research; for example, workers may be classified as exposed or nonexposed on the basis of personnel records and measurements of workplace contaminants. This classification of subjects into strata of exposed and nonexposed has analogy to experimental studies, such as clinical trials in which exposure status is controlled by the researcher. However, in epidemiologic studies of environmental agents, there may be no population that is entirely nonexposed, and the exposure may vary greatly from person to person in intensity, timing, and duration. In estimating the relative risk of disease associated with a particular environmental agent, the researcher may need to contend with multiple continuous and discrete variables, including the exposure of interest.

For example, such demanding data are encountered frequently in studying effects of environmental agents on respiratory health. Lung function, an outcome variable, is continuous whereas some predictors of interest may be (or be classified as) either discrete or continuous. Typically, this type of analytic problem is approached by modeling of the functional relations among variables. In general, a specific class of models is developed to examine functional dependence of outcome on risk factors, and a certain member or members of the class are identified as having adequately good fit to the data, or the class is rejected. For example, a common class is that of linear models, and the investigator may accept all linear models with coefficients in the calculated confidence ranges. The models include an associated distribution of differences between actual observations and those predicted by the models, and careful modeling always includes study of these differences. This approach allows for simultaneous consideration of the effects of multiple risk factors and the description of dose-response relationships for individual agents while controlling for the effects of other variables. Models in current use can accommodate both continuous and discrete risk factors.

Of course, inferences about relationships between predictor variables and outcome depend on the assumptions that a model requires regarding



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