Causal Discovery for Patient Classification Using Health-Related Quality of Life Questionnaires

Background: Health-related quality of life (HRQoL) questionnaires are essential for understanding the physical, psychological, lifestyle, and social factors that impact patients’ well-being. Causal discovery demonstrates significant potential in this direction; however, it has not yet been thoroughly assessed. This study aimed to explore the perspective of utilizing causal discovery as a methodological tool for binary classification of patients based on HRQoL questionnaire data. Methods: The focus was on questionnaire structures similar to the EQ-5D-5L, which includes both ordinal and quantitative items. A customized classification algorithm is proposed, which utilizes the differences between the causal structures derived from the HRQoL questionnaire answers of patients who belong to two distinct groups. This algorithm was evaluated using the correct classification rate (CCR) and the misclassification rate (MR) based on simulated data under conditions of varying sample size and causal structures’ complexity, and within a real-world data application. Results: In both the simulation and application, the CCR exhibited larger values compared to the MR; however, the percentages that the algorithm could not result in a decision were, in general, not negligible. The adjusted CCR (algorithm yields a decision) exhibited substantially improved values compared to the CCR in both analyses. Within the application, the algorithm showed mixed performance compared to a standard stepwise binary logistic regression approach. Conclusions: The proposed algorithm has the potential to correctly classify patients, but further investigation is needed to evaluate its performance under different scenarios in a large-scale real-world setting. Determining the necessary conditions for successful classification would result in effectively exploiting causal discovery to further advance the role of HRQoL questionnaires in patient care and management.