Brief Report: Mediation Analysis with an Ordinal Outcome

Area of Science:

• Statistics
• Biostatistics
• Epidemiology

Background:

• Mediation analysis is crucial for understanding causal pathways.
• Existing methods primarily focus on continuous or binary outcomes.
• Ordinal outcomes present unique challenges due to their ordered categorical nature.

Purpose of the Study:

• To develop and present concepts and methods for mediation analysis specifically for ordinal outcomes.
• To define natural direct and indirect effects for ordinal outcomes using counterfactuals.
• To explore different scales (difference and ratio) for quantifying these effects.

Main Methods:

• Utilized counterfactual frameworks to define direct and indirect effects for ordinal outcomes.
• Investigated confounding assumptions necessary for effect identification.
• Discussed various statistical modeling strategies for estimation, including proportional odds models.

Main Results:

• Defined natural direct and indirect effects on both difference and ratio scales for ordinal outcomes.
• Identified that under a common proportional odds model, ratio-scale effects are summarized by a single closed-form estimate.
• Demonstrated that effects may differ across outcome categories, requiring numeric simulation methods for estimation.

Conclusions:

• The proposed methods extend mediation analysis to the common scenario of ordinal outcomes.
• The proportional odds model offers a parsimonious approach for estimating ratio-scale effects.
• Careful consideration of modeling strategies is necessary for accurate mediation analysis with ordinal data.