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Computing power and sample size for informational odds ratio.

Jimmy T Efird1

  • 1Center for Health Disparities Research and Department of Public Health, Brody School of Medicine, Greenville, NC 27858, USA. jimmy.efird@stanfordalumni.org.

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Summary
This summary is machine-generated.

The informational odds ratio (IOR) offers a collapsible measure for disease association in case-referent studies. This study details calculating statistical power and sample size for unadjusted IORs, enhancing epidemiological research methods.

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Traditional odds ratios (TORs) lack collapsibility, a key property for unbiased statistical measures.
  • Informational odds ratio (IOR) is defined as post-exposure odds divided by pre-exposure odds.
  • Collapsibility ensures that adjusted ratio estimates remain consistent when controlling for non-confounding variables.

Purpose of the Study:

  • To introduce and explain the concept of the informational odds ratio (IOR).
  • To highlight the desirable property of collapsibility in ratio measures, contrasting IORs with TORs.
  • To provide methods for calculating statistical power and sample size for unadjusted IORs in case-referent studies.

Main Methods:

  • The paper defines the informational odds ratio (IOR) as the ratio of post-exposure odds to pre-exposure odds.
  • It discusses the property of collapsibility and demonstrates that adjusted IORs, particularly using the Mantel-Haenszel method, are generally collapsible.
  • The study outlines the computational approach for determining power and sample size specifically for unadjusted IORs.

Main Results:

  • Adjusted traditional odds ratios (TORs) are demonstrated to be non-collapsible.
  • Mantel-Haenszel adjusted informational odds ratios (IORs) exhibit collapsibility, similar to relative risks (RRs).
  • The paper provides a framework for calculating power and sample size for unadjusted IORs.

Conclusions:

  • Informational odds ratios (IORs) are a valuable and collapsible measure for disease association in case-referent studies.
  • The collapsibility of IORs makes them a more robust measure compared to traditional odds ratios when adjusting for variables.
  • This work facilitates the practical application of IORs by providing methods for power and sample size calculations in unadjusted scenarios.