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Common odds ratio test and interval estimation for stratified bilateral and unilateral data
Shuangcheng Hua1, Changxing Ma1
1Department of Biostatistics, University at Buffalo, NY, USA.
Statistical Methods in Medical Research
|September 11, 2024
Summary
This study introduces new statistical methods for analyzing paired medical data, even when only one side is measured. These methods accurately assess treatment effects in clinical trials with incomplete bilateral data.
Area of Science:
- Clinical Biostatistics
- Medical Data Analysis
- Statistical Inference
Background:
- Clinical research often involves bilateral data from paired organs.
- Unilateral data present challenges due to incomplete measurements.
- Existing methods may not adequately handle integrated bilateral and unilateral data.
Purpose of the Study:
- To develop statistical methods for analyzing stratified designs with combined bilateral and unilateral data.
- To provide robust inference on the common treatment effect (odds ratio).
- To address limitations in clinical data collection for paired organs.
Main Methods:
- Proposed three large-sample statistical tests.
- Developed five confidence interval methods.
- Utilized integrated bilateral and unilateral data within a stratified design.
Main Results:
- Likelihood ratio-based and score-based tests showed robust control of type I error.
- Associated confidence interval methods demonstrated close-to-nominal coverage probabilities.
- Simulations confirmed the efficacy of the proposed statistical approaches.
Conclusions:
- The proposed methods effectively handle integrated bilateral and unilateral data in stratified clinical research.
- These methods offer valid and applicable tools for analyzing real-world clinical datasets.
- Demonstrated utility in acute otitis media and myopia studies.
Keywords:
Donner’s modelScore-based confidence intervalbilateral and unilateral dataintraclass correlationodds ratiostratified randomizationMore Related Videos
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