Effect of incomplete exposure assessment on epidemiologic dose-response analyses.
OBJECTIVE
When potentially hazardous agents have multiple environmental sources, failure to include all exposure sources can constitute a type of measurement error. In addition, the effects of exposure from one source can also be confounded by exposure to other sources of the same agent. In this study clarification of these concepts is sought, and the direction and magnitude of the resulting bias in epidemiologic measures of association are examined.


METHODS
The bias in dose-response functions when the exposure data omit some sources of the agent was estimated with linear and log-linear models to compute risk differences and risk ratios under different assumptions about the magnitude and correlation of exposures from measured and unmeasured sources.


RESULTS
With unmeasured exposure of constant magnitude, there is no bias when a measure of association of the appropriate form (difference measures for additive dose-response processes, ratios for multiplicative ones) is selected. When the magnitude of unmeasured exposure varies, the results is nondifferential measurement error that can bias observed dose-response relations upward or downward, depending on the pattern of measurement error and the measure of association.


CONCLUSIONS
Failure to measure all sources of exposure to an agent and account for them in the analysis can bias the results of epidemiologic studies. When it is not feasible to measure all exposure sources, the magnitude of bias can be predicted by estimating the distribution of omitted exposures from external data or substudies. Sensitivity analyses are particularly useful for estimating the direction and magnitude of potential bias from incomplete exposure assessment.
Loomis
D P
DP
Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill 27599-7400.
Savitz
D A
DA
D015982:Bias; D015986:Confounding Factors, Epidemiologic; D004781:Environmental Exposure / Q000145:classification* / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D012307:Risk Factors