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Standardisation of rates using logistic regression: a comparison with the direct method
Andrea K Roalfe1, Roger L Holder, Sue Wilson
1Primary Care Clinical Sciences, School of Health and Population Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK. a.k.roalfe@bham.ac.uk
BMC Health Services Research
|December 31, 2008
Summary
Logistic regression offers a more reliable method for standardizing health service rates, especially with small datasets. This regression-based approach provides smoother estimates for rare disease prevalence compared to traditional direct and indirect methods.
Area of Science:
- Health Services Research
- Epidemiology
- Biostatistics
Background:
- Traditional methods for standardizing health service rates include direct and indirect arithmetic approaches.
- These methods can yield unreliable estimates when dealing with small sample sizes.
- Regression-based methods, while available, are seldom used in practice.
Purpose of the Study:
- To demonstrate the advantages of using logistic regression for standardized estimation of rare disease prevalence.
- To compare logistic regression with traditional direct standardization methods.
Main Methods:
- Employed logistic regression and the direct method for standardization.
- Utilized data from the BETS observational study on subclinical thyroid disease in the elderly.
- Standardized rates by sex and age (categorical for direct, continuous for logistic).
Main Results:
- Both methods produced estimates of similar magnitude when standardizing by age and sex.
- Logistic regression yielded lower standard errors compared to the direct method.
- Demonstrated the practical application of logistic regression in health services research.
Conclusions:
- Regression-based standardization, specifically logistic regression, is a practical and superior alternative to the direct method.
- It offers more reliable estimates, particularly with small numbers, and greater flexibility in variable selection (continuous and categorical).
- Logistic regression enables standardization in scenarios where the direct method would produce unreliable results.
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