An improved estimator of the logarithmic odds ratio for small sample sizes using a Bayesian approach
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Toru Ogura
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Takemi Yanagimoto
Abstract
The logarithmic odds ratio is a well-known method for comparing binary data between two independent groups. Although various existing methods proposed for estimating a logarithmic odds ratio, most methods estimate two proportions in each group independently and then estimate the logarithmic odds ratio using the two estimated proportions. When using a logarithmic odds ratio, researchers are more interested in the logarithmic odds ratio than proportions for each group. Parameter estimations, generally, incur random and systematic errors. These errors in initially estimated parameter may affect later estimated parameter. We propose a Bayesian estimator to directly estimate a logarithmic odds ratio without using proportions for each group. Many existing methods need to estimate two parameters (two proportions in each group) to estimate a logarithmic odds ratio; however, the proposed method only estimates one parameter (logarithmic odds ratio). Therefore, the proposed estimator can be closer to the population’s logarithmic odds ratio than existing estimators. Additionally, the validity of the proposed estimator is verified using numerical calculations and applications.
Acknowledgments
The authors wish to thank the Editor and the Reviewers for their valuable comments and suggestions, which have contributed to the revised manuscript. This study was carried out under the ISM Cooperative Research Program (2023-ISMCRP-2042).
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/ijb-2024-0105).
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Artikel in diesem Heft
- Frontmatter
- Research Articles
- Prognostic adjustment with efficient estimators to unbiasedly leverage historical data in randomized trials
- Homogeneity test and sample size of response rates for AC 1 in a stratified evaluation design
- A review of survival stacking: a method to cast survival regression analysis as a classification problem
- DsubCox: a fast subsampling algorithm for Cox model with distributed and massive survival data
- A hybrid hazard-based model using two-piece distributions
- Regression analysis of clustered current status data with informative cluster size under a transformed survival model
- Bayesian covariance regression in functional data analysis with applications to functional brain imaging
- Risk estimation and boundary detection in Bayesian disease mapping
- An improved estimator of the logarithmic odds ratio for small sample sizes using a Bayesian approach
- Short Communication
- A multivariate Bayesian learning approach for improved detection of doping in athletes using urinary steroid profiles
- Research Articles
- Guidance on individualized treatment rule estimation in high dimensions
- Weighted Euclidean balancing for a matrix exposure in estimating causal effect
- Penalized regression splines in Mixture Density Networks
