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Related Experiment Videos

Simple methods for checking for possible errors in reported odds ratios, relative risks and confidence intervals.

P N Lee1

  • 1P.N. Lee Statistics and Computing Ltd., 17 Cedar Road, Sutton, Surrey, SM2 5DA, U.K. PeterLee@pnlee.demon.co.uk

Statistics in Medicine
|August 10, 1999
PubMed
Summary
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Researchers present methods to validate odds ratios (ORs) and relative risks (RRs) in meta-analyses when source data are unavailable. Checking these statistical measures ensures accuracy in epidemiological research, preventing undetected errors.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health Research

Background:

  • Meta-analyses commonly utilize odds ratios (ORs) and relative risks (RRs) with 95% confidence intervals (CIs) from published epidemiological studies.
  • The accuracy of these reported statistical measures is crucial for the validity of meta-analysis findings.
  • Checking reported ORs, RRs, and CIs against original source data is ideal but often not feasible.

Purpose of the Study:

  • To present simple, practical methods for assessing the validity of reported ORs, RRs, and CIs when source data are inaccessible.
  • To highlight the potential for undetected errors in published epidemiological data used in meta-analyses.

Main Methods:

  • Development of methods to infer minimum study sample sizes, minimum case counts, and minimum category sizes from reported ORs/RRs and CIs.

Related Experiment Videos

  • Application of these validation techniques to published data, using environmental tobacco smoke (ETS) exposure as a case study.

    • Main Results:

    • The presented methods allow for a preliminary check of data consistency and plausibility without access to raw data.
    • Analysis of examples from environmental tobacco smoke (ETS) literature revealed that data errors are not uncommon.
    • Such errors can remain undetected in systematic reviews and meta-analyses, potentially skewing overall conclusions.

    Conclusions:

    • Simple inferential methods can aid in detecting potential errors in reported epidemiological statistics (ORs, RRs, CIs).
    • Increased vigilance and validation checks are necessary to ensure the reliability of meta-analysis results.
    • Addressing data inaccuracies is vital for advancing accurate public health research and evidence-based decision-making.