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

doi:10.1007/s10742-014-0114-0

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 journal › Article › peer-review

3 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 language	English (US)
Pages (from-to)	3-14
Number of pages	12
Journal	Health Services and Outcomes Research Methodology
Volume	14
Issue number	1-2
DOIs	https://doi.org/10.1007/s10742-014-0114-0
State	Published - Jun 2014
Externally published	Yes

All Science Journal Classification (ASJC) codes

Health Policy
Public Health, Environmental and Occupational Health

Keywords

Complex survey design
Econometrics
Logistic regression
Regression risk analysis
Risk ratios

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10.1007/s10742-014-0114-0

Cite this

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title = "Computing risk ratios from data with complex survey design",

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.",

keywords = "Complex survey design, Econometrics, Logistic regression, Regression risk analysis, Risk ratios",

author = "Norton, {Edward C.} and Carroll, {Nathan W.} and Miller, {Morgen M.} and Kasey Coyne and Wang, {Jason J.} and Kleinman, {Lawrence C.}",

year = "2014",

month = jun,

doi = "10.1007/s10742-014-0114-0",

language = "English (US)",

volume = "14",

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T1 - Computing risk ratios from data with complex survey design

AU - Norton, Edward C.

AU - Carroll, Nathan W.

AU - Miller, Morgen M.

AU - Coyne, Kasey

AU - Wang, Jason J.

AU - Kleinman, Lawrence C.

PY - 2014/6

Y1 - 2014/6

N2 - 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.

AB - 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.

KW - Complex survey design

KW - Econometrics

KW - Logistic regression

KW - Regression risk analysis

KW - Risk ratios

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JO - Health Services and Outcomes Research Methodology

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ER -

Computing risk ratios from data with complex survey design

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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