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Control Outcomes and Exposures for Improving Internal Validity of Nonrandomized Studies
Stacie B Dusetzina1,2,3,4, M Alan Brookhart4,5, Matthew L Maciejewski6,7
1Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Health Services Research
|January 20, 2015
Summary
Control outcomes and exposures enhance the internal validity of nonrandomized studies by detecting residual bias. These methods improve confidence in study findings, crucial for clinical and policy decisions.
Area of Science:
- Epidemiology
- Biostatistics
- Health Research Methods
Background:
- Nonrandomized studies are susceptible to residual bias, which can affect effect estimates.
- Control outcomes (no or opposite effect) and control exposures (no effect) are tools to assess this bias.
- Inconsistent terminology complicates the identification and application of these bias-detection methods.
Purpose of the Study:
- To review the use of control outcomes and exposures in nonrandomized studies.
- To provide recommendations for conducting and reporting analyses using these bias-detection tools.
- To enhance the internal validity and reliability of evidence from nonrandomized research.
Main Methods:
- A systematic review of studies using control outcomes or exposures was conducted.
- Searches were performed in Google Scholar and Medline.
- Data abstracted included study design, outcomes, exposures, and identified biases.
Main Results:
- Eleven studies were reviewed, with inconsistent terminology noted.
- Six studies demonstrated null associations with control outcomes/exposures, increasing confidence in findings.
- Five studies showed unexpected associations, indicating potential bias in the primary results.
Conclusions:
- Incorporating control outcomes and exposures strengthens the rigor of nonrandomized studies.
- These methods are valuable for detecting bias and improving the trustworthiness of research findings.
- Adoption of these tools is recommended for robust clinical and policy inferences.
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