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[Odds ratio or prevalence ratio? Their use in cross-sectional studies]
A Schiaffino1, M Rodríguez, M I Pasarín
1Servei de Prevenció i Control del Cáncer. Institut Català d'Oncologia. Barcelona. España.
Gaceta Sanitaria
|February 28, 2003
Summary
When estimating prevalence ratios (PR) in cross-sectional studies, calculation methods yield similar results for low prevalence (<20%). However, differences emerge with high prevalence (>20%), impacting estimators and confidence intervals.
Area of Science:
- Epidemiology
- Biostatistics
Context:
- Cross-sectional studies frequently employ measures of association like the odds ratio (OR) and prevalence ratio (PR).
- Inconsistent reporting practices exist where OR is stated but PR is calculated, necessitating clear methodological understanding.
- Prevalence estimation is critical for understanding disease burden and informing public health interventions.
Purpose:
- To systematically review and compare various statistical methods for calculating the prevalence ratio (PR) in cross-sectional research.
- To evaluate the performance of different PR calculation techniques under varying prevalence conditions (low <20% vs. high >20%).
- To provide guidance on selecting appropriate PR estimation methods based on study data characteristics.
Summary:
- Four common methods for estimating PR were identified: logistic regression (OR definition), Breslow-Cox regression, generalized linear models, and OR-to-PR conversion.
- Simulations using real-world data demonstrated minimal discrepancies in estimators and standard errors when prevalence was low.
- Significant differences in estimators and confidence intervals were observed for high prevalence scenarios, though statistical significance was generally maintained across methods.
Impact:
- Highlights the importance of method selection in PR estimation, particularly when dealing with high prevalence data.
- Informs researchers about potential variations in results based on chosen statistical techniques.
- Encourages consistent and appropriate application of PR calculation methods for accurate epidemiological inference.
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