49: Interpretable Ordinal Analysis for Complex Designs in Cell and Molecular Biology

Music City Center 

Visual scoring is widely used in biomedical research to translate complex biological traits into ordered datasets suitable for hypothesis testing. Although advanced statistical methods exist for analyzing ordered data, use of ordinal methods by researchers remains limited. Parameter estimates from ordinal regression models, such as odds ratios or differences in probits, can hinder adoption due to their interpretive complexity. Recently, summary measures for ordinal regression models have been proposed to improve interpretability. In this work, we demonstrate the application of the γ (gamma) and ∆ (delta) ordinal superiority measures to more complex experimental designs, including interactions and multicategorical explanatory variables. Using an example dataset on cellular stress response phenotypes, we illustrate how these measures can be utilized in complex experimental designs to yield clear, meaningful interpretations of ordinal regression analyses. By demonstrating real-world applicability, this work provides a practical resource for biological researchers working with ordered response data and promotes broader adoption of ordinal regression techniques in biomedical studies.

Ordinal data

Ordinal Regression

Cumulative Link Models

Interaction Terms

Proportional Odds

Ordinal Superiority Measure 

Section on Statistics in Genomics and Genetics