Stereotyping and the treatment of missing data for drug and alcohol clinical trials
2009

Stereotyping in Drug and Alcohol Clinical Trials

Sample size: 34 Editorial Evidence: low

Author Information

Author(s): Arndt Stephan

Primary Institution: University of Iowa

Hypothesis

How do clinical trials in addiction research treat missing data?

Conclusion

Many studies incorrectly assume that clients who drop out of treatment have relapsed, which can introduce bias and perpetuate negative stereotypes.

Supporting Evidence

  • 32.4% of the reviewed studies used static imputation to fill in missing data.
  • Only 20.6% of articles indicated the use of more appropriate statistical treatments for missing data.
  • Many studies lack a statistical rationale for assuming dropouts have relapsed.

Takeaway

When people drop out of addiction studies, researchers often guess they relapsed, which isn't always true and can lead to wrong conclusions.

Methodology

An informal review of 34 clinical trial reports from four prominent journals was conducted to assess how missing data were handled.

Potential Biases

Assuming dropouts have relapsed introduces bias and supports negative stereotypes.

Limitations

The review was informal and may not represent all clinical trials in substance abuse research.

Digital Object Identifier (DOI)

10.1186/1747-597X-4-2

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