MIC*: A Framework for Interpretable Analysis of Ordinal Viability Data

Microbial survival assays frequently use ordered categorical (ordinal) scores, such as semi-quantitative viability scores across drug concentrations. While these ordinal data provide rich information about dose-response dynamics, they require appropriate statistical approaches for proper analysis. Proportional-odds (PO) ordinal regression is specifically designed for such data, modeling the ordered nature of scores while accommodating continuous variables such as concentration. However, despite its advantages, PO regression remains underutilized in microbiology because its cumulative log-odds outputs are abstract and biologically unintuitive. Consequently, researchers often resort to flawed alternatives: collapsing scores into binary outcomes (growth/no growth), treating scores as continuous values for t-tests or ANOVA, or applying nonparametric tests that ignore dose-response structure. Each approach sacrifices power or validity, risking unreliable conclusions. To allow researchers to better leverage the power of ordinal datasets, we introduce MIC*, a summary measure that translates PO regression results into biologically interpretable units. MIC* is defined as the treatment concentration where the predicted probability of "no viability" equals 0.5, and is thus conceptually related to the minimum inhibitory dose (MIC) widely used in antimicrobial research. MIC* retains the rigor of ordinal regression while providing more biologically intuitive effect size measures. The MIC* framework enables formal comparisons as absolute differences (ΔMIC*) or relative fold-changes (Δlog ₂ MIC*), allowing for robust statistical comparisons between samples. Monte Carlo simulations demonstrate that MIC* yields robust estimates with superior power compared to conventional statistical methods, while case studies demonstrate practical utility. To ensure wide adoption, we provide a suite of open-source tools: a BLInded Scoring System (BLISS) for generating ordinal viability scores, and web-based (MICalculator) and R package (ordinalMIC) alternatives for performing complete MIC* analyses, thus reducing barriers to appropriate analysis of ordinal phenotypes in microbiology.