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Notes on interval estimation of the generalized odds ratio under stratified random sampling.
Kung-Jong Lui1, Kuang-Chao Chang
1Department of Mathematics and Statistics , San Diego State University , San Diego , CA , USA. kjl@rohan.sdsu.edu
Journal of Biopharmaceutical Statistics
|April 25, 2013
Summary
This study evaluates four interval estimators for the generalized odds ratio (GOR) in randomized clinical trials with ordinal outcomes. The Mantel-Haenszel (MHL) method shows the least bias and good performance for analyzing ordinal patient responses.
Area of Science:
- Biostatistics
- Clinical Trials
- Statistical Modeling
Background:
- Ordinal patient responses are common in randomized clinical trials (RCTs).
- Accurate estimation of the generalized odds ratio (GOR) is crucial for analyzing such data.
- Stratified random sampling requires appropriate interval estimators for GOR.
Purpose of the Study:
- To evaluate and compare four asymptotic interval estimators for the GOR under stratified random sampling.
- To assess the performance of these estimators regarding coverage probability, average length, and bias.
- To identify the most reliable estimator for ordinal outcomes in RCTs.
Main Methods:
- Considered four interval estimators: WLSL, MHL, FTMH, and FTWLS.
- Employed Monte Carlo simulations to assess performance metrics.
- Calculated coverage probability, average length, and noncoverage probabilities for bias analysis.
Main Results:
- WLSL and MHL generally performed well.
- FTMH and FTWLS exhibited potential loss of precision or accuracy.
- MHL was identified as the least biased estimator.
Conclusions:
- MHL and WLSL are recommended for analyzing ordinal outcomes in stratified RCTs.
- MHL offers a good balance of accuracy and precision with minimal bias.
- The study illustrates practical application using real-world smoking and breathing test data.
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