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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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Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
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Counting Proteins in Single Cells with Addressable Droplet Microarrays
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Addressing unmeasured confounding in comparative observational research.

Xiang Zhang1, Douglas E Faries1, Hu Li1

  • 1Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA.

Pharmacoepidemiology and Drug Safety
|February 1, 2018
PubMed
Summary

Unmeasured confounding in observational studies can bias results. This study provides a best practice recommendation and tools to help researchers quantitatively assess and address unmeasured confounding.

Keywords:
best practice recommendationpharmacoepidemiologysensitivity analysessystematic reviewunmeasured confounding

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Area of Science:

  • Pharmacoepidemiology
  • Observational Research Methods

Background:

  • Observational pharmacoepidemiological studies offer real-world insights into intervention effectiveness and safety.
  • A significant challenge in these studies is the presence of unmeasured confounders, which can bias results.
  • Existing analytical methods to address unmeasured confounding are under-utilized due to complexity.

Purpose of the Study:

  • To address the unmet need for improved understanding of how to handle unmeasured confounding in various research scenarios.
  • To develop a best practice recommendation for selecting appropriate analytical methods to assess unmeasured confounding.
  • To guide researchers in quantitatively evaluating the impact of unmeasured confounding.

Main Methods:

  • A stepwise literature search was conducted to identify methods for assessing unmeasured confounding.
  • Identified publications were reviewed and characterized by research settings and implementation requirements.
  • A flowchart and checklist were developed to support the best practice recommendation.

Main Results:

  • Over 100 papers were reviewed, leading to the identification of 15 distinct methods.
  • A best practice recommendation flowchart was created, driven by confounder information availability and assessment goals.
  • A checklist summarizing key implementation factors for each method was developed.

Conclusions:

  • The impact of unmeasured confounding must be considered in comparative effectiveness and safety assessments from observational research.
  • Quantitative evaluation of unmeasured confounding is recommended.
  • A best practice recommendation is provided to aid in selecting appropriate analytical methods for unmeasured confounding.