Computing risk ratios from data with complex survey design

Edward C. Norton, Nathan W. Carroll, Morgen M. Miller, Kasey Coyne, Jason J. Wang, Lawrence C. Kleinman

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We demonstrate how to apply regression risk analysis to compute risk ratios and risk differences from logistic regression models using complex survey data. We validate the use of regression risk analysis for complex survey design. First, we derive formulas for adjusted risk ratios (ARRs) and adjusted risk differences (ARDs) adjusted for weighting, stratification, and clustering. Then we use Monte Carlo data to show why correcting statistics for complex survey design is important. We show how to calculate and interpret ARRs using a publicly available data set with a binary outcome. Regression risk analysis can be applied to complex survey data to calculate correct ARRs and ARDs from logistic regression.

Original languageEnglish
Pages (from-to)3-14
Number of pages12
JournalHealth Services and Outcomes Research Methodology
Volume14
Issue number1-2
DOIs
StatePublished - Jun 2014

Keywords

  • Complex survey design
  • Econometrics
  • Logistic regression
  • Regression risk analysis
  • Risk ratios

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