Exact correction factor for estimating the OR in the presence of sparse data with a zero cell in 2 × 2 tables

Babu Malavika, Mani Thenmozhi, Sappani Marimuthu, George Sebastian, Bangdiwala Shrikant I. and Jeyaseelan Lakshmanan ()
Additional contact information
Babu Malavika: Department of Biostatistics, Christian Medical College, Vellore, Tamil Nadu, India
Mani Thenmozhi: Department of Biostatistics, Christian Medical College, Vellore, Tamil Nadu, India
Sappani Marimuthu: Department of Biostatistics, Christian Medical College, Vellore, Tamil Nadu, India
George Sebastian: Department of Statistical Sciences, Kannur University, Kannur, Kerala, India
Bangdiwala Shrikant I.: Department of Health Research Methods, Evidence and Impact, McMaster University Faculty of Health Sciences, Hamilton, ON, Canada
Jeyaseelan Lakshmanan: College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates

The International Journal of Biostatistics, 2024, vol. 20, issue 1, 229-243

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.

Keywords: correction factor; coverage probability; odds ratio; RMSE; sparsity (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/ijb-2022-0040 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:20:y:2024:i:1:p:229-243:n:1005

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.1515/ijb-2022-0040

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().