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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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Published on: September 27, 2019

Differences between marginal structural models and conventional models in their exposure effect estimates: a

David Suarez1, Roger Borràs, Xavier Basagaña

  • 1Epidemiology and Assessment Unit, Fundació Parc Tauli, Universitat Autònoma de Barcelona, Sabadell, Spain. david.suarez.lamas@gmail.com

Epidemiology (Cambridge, Mass.)
|May 5, 2011
PubMed
Summary

Marginal structural models (MSMs) and conventional models yield different results in real-world studies. Improved reporting of MSMs, especially regarding stabilized inverse-probability weights, is necessary for accurate causal inference.

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Last Updated: Jun 2, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Marginal structural models (MSMs) were developed to address time-varying confounding in nonrandomized studies.
  • The real-world differences between MSM and conventional model estimates remain unclear.

Purpose of the Study:

  • To systematically review the literature on MSMs since 2000.
  • To compare estimates from MSMs and conventional models in real-world settings.
  • To assess the reporting quality of MSMs in published literature.

Main Methods:

  • Systematic literature review of MSMs published since 2000.
  • Analysis of 164 exposure-outcome associations from 65 papers.
  • Comparison of effect estimates between MSMs and conventional models.

Main Results:

  • Estimates differed by at least 20% in 40% of studies.
  • 11% of studies showed opposite interpretations between the two model types.
  • Reporting of stabilized inverse-probability weights and their validation was suboptimal in many MSM applications.

Conclusions:

  • Significant discrepancies exist between MSM and conventional model estimates in real studies.
  • The application and reporting of MSMs require improvement for reliable causal inference.
  • Enhanced methodological rigor in MSM application is crucial for advancing epidemiological research.