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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Model-based standardization to adjust for unmeasured cluster-level confounders with complex survey data.

Zhuangyu Cai1, Babette A Brumback1

  • 1Department of Biostatistics, University of Florida, Gainesville, FL, 32611, U.S.A.

Statistics in Medicine
|April 9, 2015
PubMed
Summary

This study introduces two novel methods for model-based standardization using complex survey data. These methods effectively adjust for categorical confounders in large subgroup populations, improving health outcome comparisons.

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composite likelihoodconditional likelihoodconfoundinggeneralized linear mixed modelsmodel-based standardization

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survey Methodology

Background:

  • Model-based standardization estimates population-averaged effects, enabling group comparisons under identical confounder distributions.
  • Complex survey data often feature categorical confounders that create numerous subgroups, posing analytical challenges.
  • Existing methods may not adequately address the complexities of large numbers of subgroups in standardization.

Purpose of the Study:

  • To develop and compare two novel methods for model-based standardization tailored for complex survey data.
  • To accommodate categorical confounders that cluster observations into a large number of subgroups.
  • To estimate standardized health outcome proportions, adjusting for individual and cluster-level confounders.

Main Methods:

  • Method 1: Combines a random-intercept generalized linear mixed model with a conditional pseudo-likelihood estimator.
  • Method 2: Integrates a between-within generalized linear mixed model with cluster-level covariate data.
  • Simulation studies were conducted to evaluate and compare the performance of the two proposed methods.

Main Results:

  • The study successfully developed two distinct approaches for model-based standardization with complex survey data.
  • Both methods demonstrated the ability to handle categorical confounders within numerous subgroups.
  • Application to the 2008 Florida Behavioral Risk Factor Surveillance System data yielded standardized alcohol consumption proportions.

Conclusions:

  • The developed methods offer robust solutions for model-based standardization in the presence of complex survey designs and categorical confounders.
  • These techniques allow for more accurate comparisons of population subgroups by adjusting for both measured and unmeasured confounders.
  • The findings provide valuable tools for epidemiological research and public health surveillance utilizing complex survey data.