title: Overcoming ecologic bias using the two-phase study design.
abstract: 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.
authors.affiliation: Department of Statistics, University of Washington, Seattle, WA 98195-7232, USA. jonno@u.washington.edu
authors.forename: Jon
authors.initials: J
authors.lastname: Wakefield
authors.forename: Sebastien J-P A
authors.initials: SJ
authors.lastname: Haneuse
mesh_terms: D015982:Bias; D001724:Birth Weight; D016022:Case-Control Studies; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D015340:Epidemiologic Research Design; D005260:Female; D005843:Geography; D006801:Humans; D007226:Infant Mortality / Q000639:trends*; D007231:Infant, Newborn; D015233:Models, Statistical; D009657:North Carolina / Q000453:epidemiology; D011247:Pregnancy; D012307:Risk Factors