Design and analysis of multilevel analytic studies with applications to a study of air pollution.
We discuss a hybrid epidemiologic design that aims to combine two approaches to studying exposure-disease associations. The analytic approach is based on comparisons between individuals, e.g., case-control and cohort studies, and the ecologic approach is based on comparisons between groups. The analytic approach generally provides a stronger basis for inference, in part because of freedom from between-group confounding and better quality data, but the ecologic approach is less susceptible to attenuation bias from measurement error and may provide greater variability in exposure. The design we propose entails selection of a number of groups and enrollment of individuals within each group. Exposures, outcomes, confounders, and modifiers would be assessed on each individual; but additional exposure data might be available on the groups. The analysis would then combine the individual-level and the group-level comparisons, with appropriate adjustments for exposure measurement errors, and would test for compatibility between the two levels of analysis, e.g., to determine whether the associations at the individual level can account for the differences in disease rates between groups. Trade-offs between numbers of groups, numbers of individuals, and the extent of the individual and group measurement protocols are discussed in terms of design efficiency. These issues are illustrated in the context of an on-going study of the health effects of air pollution in southern California, in which 12 communities with different levels and types of pollution have been selected and 3500 school children are being enrolled in a ten-year cohort study.(ABSTRACT TRUNCATED AT 250 WORDS)
Department of Preventive Medicine, University of Southern California, Los Angeles 90033-9987.
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Navidi
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Thomas
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Stram
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Peters
D000293:Adolescent; D000393:Air Pollutants / Q000009:adverse effects; D000397:Air Pollution / Q000009:adverse effects* / Q000706:statistics & numerical data*; D000465:Algorithms; D015982:Bias; D002140:California / Q000453:epidemiology; D016022:Case-Control Studies; D002648:Child; D002675:Child, Preschool; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D004463:Ecology; D004781:Environmental Exposure; D004784:Environmental Monitoring; D062665:Epidemiological Monitoring; D004813:Epidemiology; D005240:Feasibility Studies; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D012044:Regression Analysis; D015203:Reproducibility of Results
D000393:Air Pollutants