Sensitivity analyses for ecological regression.
In many ecological regression studies investigating associations between environmental exposures and health outcomes, the observed relative risks are in the range 1.0-2.0. The interpretation of such small relative risks is difficult due to a variety of biases--some of which are unique to ecological data, since they arise from within-area variability in exposures/confounders. The potential for residual spatial dependence, due to unmeasured confounders and/or data anomalies with spatial structure, must also be considered, though it often will be of secondary importance when compared to the likely effects of unmeasured confounding and within-area variability in exposures/confounders. Methods for addressing sensitivity to these issues are described, along with an approach for assessing the implications of spatial dependence. An ecological study of the association between myocardial infarction and magnesium is critically reevaluated to determine potential sources of bias. It is argued that the sophistication of the statistical analysis should not outweigh the quality of the data, and that finessing models for spatial dependence will often not be merited in the context of ecological regression.
Wakefield
Jon
J
Department of Statistics, University of Washington, Seattle, Washington 98195-7232, USA. jon@stat.washington.edu
D015982:Bias; D015986:Confounding Factors, Epidemiologic; D003625:Data Collection; D003627:Data Interpretation, Statistical; D004463:Ecology / Q000379:methods*; D004781:Environmental Exposure; D004812:Epidemiologic Methods; D006801:Humans; D008274:Magnesium / Q000008:administration & dosage; D009203:Myocardial Infarction / Q000401:mortality; D012044:Regression Analysis; D012306:Risk; D012680:Sensitivity and Specificity; D017062:Small-Area Analysis
D008274:Magnesium