Overcoming Ecologic Bias using the Two-Phase Study Design

摘要

Ecologic (aggregate) data are widely available and widely utilized in epidemiologic studies. However, ecologic bias, which arises because aggregate data cannot characterize within-group variability in exposure and confounder variables, can only be removed by supplementing ecologic data with individual-level data. Here the authors describe the two-phase study design as a framework for achieving this objective. In phase 1, outcomes are stratified by any combination of area, confounders, and error-prone (or discretized) versions of exposures of interest. Phase 2 data, sampled within each phase 1 stratum, provide accurate measures of exposure and possibly of additional confounders. The phase 1 aggregate-level data provide a high level of statistical power and a cross-classification by which individuals may be efficiently sampled in phase 2. The phase 2 individual-level data then provide a control for ecologic bias by characterizing the within-area variability in exposures and confounders. In this paper, the authors illustrate the two-phase study design by estimating the association between infant mortality and birth weight in several regions of North Carolina for 2000–2004, controlling for gender and race. This example shows that the two-phase design removes ecologic bias and produces gains in efficiency over the use of case-control data alone. The authors discuss the advantages and disadvantages of the approach.