Divergent biases in ecologic and individual-level studies.
Several authors have shown that ecologic estimates can be biased by effect modification and misclassification in a different fashion from individual-level estimates. This paper reviews and discusses ecologic biases induced by model misspecification; confounding; non-additivity of exposure and covariate effects (effect modification); exposure misclassification; and non-comparable standardization. Ecologic estimates can be more sensitive to these sources of bias than individual-level estimates, primarily because ecologic estimates are based on extrapolations to an unobserved conditional (individual-level) distribution. Because of this sensitivity, one should not rely on a single regression model for an ecologic analysis. Valid ecologic estimates are most feasible when one can obtain accurate estimates of exposure and covariate means in regions with internal exposure homogeneity and mutual covariate comparability; thus, investigators should seek out such regions in the design and analysis of ecologic studies.
Greenland
S
S
Department of Epidemiology, UCLA School of Public Health 90024-1772.
D015982:Bias; D015986:Confounding Factors, Epidemiologic; D004463:Ecology; D015987:Effect Modifier, Epidemiologic; D004781:Environmental Exposure; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity