Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Discussing hidden bias in observational studies.

P R Rosenbaum1

  • 1Wharton School, University of Pennsylvania, Philadelphia.

Annals of Internal Medicine
|December 1, 1991
PubMed
Summary

Observational studies can show different outcomes due to overt or hidden bias. Sensitivity analysis quantifies how much hidden bias is needed to explain these differences, aiding in bias assessment.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Matching One Sample According to Two Criteria in Observational Studies.

Journal of the American Statistical Association·2023
Same author

Development of a measure of family-centred care for resource-poor South African settings: the experience of using a modified version of the MPOC-20.

Child: care, health and development·2008
Same author

Matching and thick description in an observational study of mortality after surgery.

Biostatistics (Oxford, England)·2003
Same author

Multivariate matching and bias reduction in the surgical outcomes study.

Medical care·2001
Same author

Reduced sensitivity to hidden bias at upper quantiles in observational studies with dilated treatment effects.

Biometrics·2001
Same author

Substantial gains in bias reduction from matching with a variable number of controls.

Biometrics·2000

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Observational studies and nonrandomized experiments risk confounding outcomes.
  • Group incomparability can arise from measured (overt) or unmeasured (hidden) biases.

Purpose of the Study:

  • To introduce and explain the concept of sensitivity analysis for hidden bias.

Main Methods:

  • Defining overt bias and its control through adjustments like matching.
  • Defining hidden bias as a more challenging issue due to unmeasured confounders.
  • Describing sensitivity analysis as a method to quantify the impact of potential hidden bias.

Main Results:

  • Sensitivity analysis provides a framework to assess the magnitude of hidden bias.
  • It allows for a tangible discussion of unmeasured confounding.

Conclusions:

  • Sensitivity analysis is a crucial tool for evaluating the robustness of findings from observational studies.
  • It helps researchers understand the potential impact of unmeasured factors on study outcomes.

Related Experiment Videos