ArticlePloS one2025
Analysis of categorical data from biological experiments with logistic regression and CMH tests.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Functional Analysis of Late-Onset Alzheimer's Disease Risk Genes inbioRxiv : the preprint server for biology · 2026Article
- Stress-associated High Production of Large Extracellular Vesicles in the Parent Generation is Not Inherited by microPublication biology · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
The choice of appropriate statistical tests in experimental biology is critical for scientific rigor and can be challenging in the case of categorical data analysis. Using example datasets from Caenorhabditis elegans research, we conduct statistical analysis of (1) a rare cellular event involving the formation of a neuronal extrusion called an exopher and (2) a variable behavioral response across time. We employ the Cochran-Mantel-Haenszel (CMH) test and logistic regression for analysis. Recognizing there are potential accessibility issues using logistic regression, we provide step-by-step tutorials and example code. We emphasize that logistic regression can handle both simple and complex multivariable datasets; logistic regression can also provide more comprehensive insights into experimental outcomes when compared to simpler tests like CMH. By analyzing real biological examples and demonstrating their analysis with R code, we provide a practical guide for biologists to enhance the rigor and reproducibility of categorical data analysis in experimental studies.
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