Exact correction factor for estimating the OR in the presence of sparse data with a zero cell in 2 × 2 tables
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Malavika Babu
Abstract
In case-control studies, odds ratios (OR) are calculated from 2 × 2 tables and in some instances, we observe small cell counts or zero counts in one of the cells. The corrections to calculate the ORs in the presence of empty cells are available in literature. Some of these include Yates continuity correction and Agresti and Coull correction. However, the available methods provided different corrections and the situations where each could be applied are not very apparent. Therefore, the current research proposes an iterative algorithm of estimating an exact (optimum) correction factor for the respective sample size. This was evaluated by simulating data with varying proportions and sample sizes. The estimated correction factor was considered after obtaining the bias, standard error of odds ratio, root mean square error and the coverage probability. Also, we have presented a linear function to identify the exact correction factor using sample size and proportion.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/ijb-2022-0040).
© 2023 Walter de Gruyter GmbH, Berlin/Boston
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Articles in the same Issue
- Frontmatter
- Research Articles
- Survival analysis using deep learning with medical imaging
- Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions
- Approximate reciprocal relationship between two cause-specific hazard ratios in COVID-19 data with mutually exclusive events
- Sensitivity of estimands in clinical trials with imperfect compliance
- Highly robust causal semiparametric U-statistic with applications in biomedical studies
- Hierarchical Bayesian bootstrap for heterogeneous treatment effect estimation
- Penalized logistic regression with prior information for microarray gene expression classification
- Bayesian learners in gradient boosting for linear mixed models
- Unequal allocation of sample/event sizes with considerations of sampling cost for testing equality, non-inferiority/superiority, and equivalence of two Poisson rates
- HiPerMAb: a tool for judging the potential of small sample size biomarker pilot studies
- Heterogeneity in meta-analysis: a comprehensive overview
- On stochastic dynamic modeling of incidence data
- Power of testing for exposure effects under incomplete mediation
- Exact correction factor for estimating the OR in the presence of sparse data with a zero cell in 2 × 2 tables
- Right-censored partially linear regression model with error in variables: application with carotid endarterectomy dataset
- Assessing HIV-infected patient retention in a program of differentiated care in sub-Saharan Africa: a G-estimation approach
- Prediction-based variable selection for component-wise gradient boosting
