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Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

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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Confounding in Epidemiological Studies

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Observational Studies

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Related Experiment Video

Updated: Jul 5, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Marginal structural models might overcome confounding when analyzing multiple treatment effects in observational

David Suarez¹, Josep Maria Haro, Diego Novick

¹Research & Development Unit, Sant Joan de Déu-SSM, Fundació Sant Joan de Déu, RETICS RD06/0011(REM-TAP Network), Sant Boi, Barcelona, Spain. david.suarez.lamas@gmail.com

Journal of Clinical Epidemiology

|May 13, 2008

Summary

Marginal structural models (MSMs) can now compare multiple treatments in observational studies. This method, using inverse-probability of treatment weights, offers improved confounding control for schizophrenia treatment outcomes.

Related Experiment Videos

Last Updated: Jul 5, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

Epidemiology
Biostatistics
Psychiatric Research

Background:

Observational studies are crucial for understanding treatment effects but face confounding challenges.
Marginal structural models (MSMs) are statistical tools designed to address time-varying confounding.
Previous applications of MSMs were limited to comparing two treatments.

Purpose of the Study:

To review and demonstrate the utility of MSMs for comparing multiple treatment effects.
To extend the application of MSMs beyond two-treatment comparisons in observational research.
To assess the effectiveness of antipsychotic medications on schizophrenia remission using MSMs.

Main Methods:

Reanalysis of the SOHO study data, a 3-year observational study on schizophrenia treatment outcomes.
Application of marginal structural models (MSMs) to compare effects of multiple antipsychotic medications.
Utilizing inverse-probability of treatment weights within the MSM framework to adjust for confounding.

Main Results:

MSM results generally aligned with conventional methods but showed reduced statistical significance.
Qualitative differences emerged in some comparisons where conventional analysis contradicted prior knowledge.
MSMs demonstrated potential for improved control of confounding compared to traditional approaches.

Conclusions:

Marginal structural models (MSMs) are applicable for analyzing multiple treatment effects in observational studies.
MSMs, via inverse-probability of treatment weights, may offer superior confounding control.
This approach can enhance the reliability of observational study findings, approximating results from randomized controlled trials.