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Related Experiment Video

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Mouse Model of Surgical Uterine Injury and Subsequent Pregnancy Outcomes

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Published on: June 27, 2025

Model-based standardization using an outcome model with random effects.

Zhongkai Wang¹, Babette A Brumback¹, Adel A Alrwisan²

¹Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida.

Statistics in Medicine

|June 1, 2019

Summary

This summary is machine-generated.

This study introduces a novel outcome modeling approach for model-based standardization, effectively adjusting for complex confounding factors. The method enhances population-average association estimation in observational studies.

Keywords:

causal inference confounding generalized linear mixed models marginal effect model-based standardization

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Area of Science:

Epidemiology
Biostatistics
Health Services Research

Background:

Model-based standardization is crucial for estimating population-average associations.
Existing methods may struggle with complex confounding structures, particularly with numerous clusters.

Purpose of the Study:

To develop and evaluate a novel outcome modeling approach for model-based standardization.
To address confounding in large-scale observational data with clustered observations.

Main Methods:

Developed an outcome model treating cluster parameters as random effects.
Employed a between-within model to handle associations between random effects, exposure, and cluster sizes.
Compared the proposed method with existing exposure-modeling approaches.

Main Results:

The proposed outcome-modeling approach effectively adjusts for confounding.
Demonstrated utility in comparing acute respiratory tract infection antibiotic prescription rates between emergency and outpatient settings.
Simulation studies confirmed the method's validity and performance.

Conclusions:

The developed outcome-modeling approach offers a robust method for model-based standardization.
This technique is valuable for unconfounded estimation of population-average associations in complex datasets.
The approach provides a significant advancement for epidemiological and health services research.