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An R-Based Landscape Validation of a Competing Risk Model
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Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data.

Gayle S Bieler1, G Gordon Brown, Rick L Williams

  • 1Statistics and Epidemiology Unit, RTI International, Research Triangle Park, North Carolina 27709-2194, USA. gbmac@rti.org

American Journal of Epidemiology
|February 6, 2010
PubMed
Summary
This summary is machine-generated.

This study demonstrates how to estimate population-based risk differences and ratios using logistic regression models with complex sample survey data. These methods provide more direct inferences than traditional odds ratios for prevalence studies.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survey Methodology

Background:

  • Increasing interest in estimating risk or prevalence ratios and differences over odds ratios in regression analyses.
  • Previous methods focused on non-population-based studies using SAS GENMOD procedure.
  • Need for population-based inferences from complex sample surveys.

Purpose of the Study:

  • To demonstrate obtaining model-adjusted risks, risk differences, and risk ratios directly from logistic regression models.
  • To enable population-based inferences from complex sample survey data.
  • To extend regression-based risk estimation to complex survey settings.

Main Methods:

  • Utilizing logistic regression models with average marginal predictions for point estimates.
  • Incorporating complex sample survey designs (weighting, stratification, clustering).
  • Employing SUDAAN software for estimates, standard errors, confidence intervals, and P-values.
  • Using data from the 2006 National Health Interview Survey for illustration.

Main Results:

  • Successful estimation of model-adjusted risks, risk differences, and risk ratios from complex survey data.
  • Demonstration of obtaining population-based inferences.
  • Application of both continuous and categorical covariates, including interaction terms.

Conclusions:

  • Logistic regression models can directly yield population-based risk estimates from complex sample surveys.
  • This approach offers a valuable alternative to odds ratios for prevalence and risk studies.
  • SUDAAN facilitates robust statistical inference for complex survey data analysis.