Work

Trend Analysis of Aggregate Outcomes in Complex Health Survey Data.

Year: 2018

Type: article

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 toweighted 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.

Source: Europe PMC (PubMed Central)

Authors: Wesley M Durand, Paul C Stey, Elizabeth S Chen, Indra Neil Sarkar

Institution: Brown University

Language: English

Topic: Survey Sampling and Estimation Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institute of General Medical Sciences

Award: Advance Clinical and Translational Research (Advance-CTR)