SUFFICIENT CAUSE INTERACTIONS FOR CATEGORICAL AND ORDINAL OUTCOMES

Area of Science:

• Epidemiology
• Biostatistics
• Causal Inference

Background:

• The binary sufficient cause model is foundational for understanding causality.
• Extending causal inference models to non-binary outcomes is crucial for comprehensive analysis.
• Detecting interactions and synergism in complex health outcomes requires advanced statistical frameworks.

Purpose of the Study:

• To extend the sufficient cause model to categorical and ordinal outcomes.
• To formalize the concepts of sufficient cause interaction and synergism for these outcome types.
• To develop novel empirical conditions and statistical tests for detecting such interactions.

Main Methods:

• Extension of the sufficient cause model framework.
• Derivation of counterfactual and empirical conditions for interaction.
• Development and application of likelihood ratio tests for sufficient cause interaction.
• Application to HIV drug resistance data.

Main Results:

• Novel conditions for detecting sufficient cause interactions in ordinal and categorical outcomes were derived.
• These conditions are distinct from those applicable to binary outcomes.
• Likelihood ratio tests were successfully developed and applied.
• Sufficient cause interaction between two HIV resistance mutations was detected.

Conclusions:

• The extended sufficient cause model provides a robust framework for analyzing interactions with non-binary outcomes.
• The novel empirical conditions and tests facilitate the identification of synergistic effects in complex scenarios.
• This approach has significant implications for understanding disease mechanisms and treatment resistance, as demonstrated in the HIV drug resistance example.