Check an ordinal proportional-odds analysis

Verify that the categories are ordered

An ordinal outcome has categories with a meaningful order but not necessarily equal spacing. Begin with the instrument definition and the exact coding used in the dataset. Alphabetical sorting can silently reverse or scramble the intended order. Preserve empty or rare categories deliberately and explain any scientifically justified collapsing before examining treatment effects.

The statsmodels ordinal-regression documentation illustrates ordered-category models and their parameterisation. Read the software's direction convention before interpreting a positive coefficient: different descriptions of cumulative probabilities can reverse the apparent sign.

State the proportional-odds assumption

A proportional-odds model uses a common predictor effect across cumulative category thresholds. Write the threshold comparisons in words, such as at least moderate versus lower, and severe versus lower. Ask whether a common contrast is scientifically plausible across them.

Inspect category counts and predicted probabilities. Compare threshold-specific patterns using suitable diagnostics, while recognising that sparse categories can make assumption tests unreliable. A nonsignificant diagnostic does not establish that the common-effect summary is correct. Conversely, a detected departure should prompt an interpretable alternative rather than automatic dichotomisation of the outcome.

Hypothetical example

A fictional outcome has mild, moderate and severe categories. Suppose an exploratory threshold analysis suggests an exposure odds ratio of two for moderate-or-severe versus mild, but one-half for severe versus mild-or-moderate. These opposite directions would make a single common odds ratio difficult to interpret.

The example does not provide an estimate for a real study or prescribe a particular alternative model. It shows why one summary number should be checked against the distribution it is intended to describe.

Report the model in the outcome's language

Specify category order, the link, covariates and the direction of the reported odds ratio. Show observed category counts and useful predicted probabilities with uncertainty where appropriate. Explain the assumption checks, any alternative specification and the handling of missing categories or responses.

Do not interpret a cumulative odds ratio as the relative probability of being in one specific category. Use the categorical recode crosswalk to retain original labels and ordered codes. If a more flexible model is needed, document its additional parameters and whether the available sample supports them. The final result should remain understandable without requiring readers to infer the software's internal threshold convention.

Source verification: 6 November 2026. Examples are hypothetical.