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Abstract: Public health and clinical practice pattern trends are often analyzed using complex survey data. Use of statistical approaches that do not account for survey design predisposes to error, potentially leading to resource misdirection and inefficiency. This study examined two techniques for analyzing trends in complex survey data: (1) design-corrected logistic regression and (2) jackknife re-weighted linear regression. These approaches were compared to weighted least squares regression, as well as non-design corrected techniques. Data were obtained from NEISS, a complex survey of emergency departments that can be weighted to produce national estimates of injury occurrence. Trends were analyzed in rug-related injuries among male versus female patients ≥65 years of age. All design-corrected techniques performed comparably in assessment of trend within sex-based subgroups. In almost all cases, design-corrected approaches contrasted profoundly with standard statistical techniques. Future analyses may employ these design-corrected approaches to appropriately account for estimate variance in complex survey data.

Learning Objective 1: Understand the use of design-corrected statistical techniques to analyze trends in aggregate outcomes from complex survey data.

Authors:

Wesley Durand, Brown University
Paul Stey (Presenter)
Brown University

Elizabeth Chen, Brown University
Indra Sarkar, Brown University

Presentation Materials:

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