Latent Factor Decomposition Model: Applications for Questionnaire Data
This work addresses the problem of robust and interpretable analysis of clinical questionnaire data for researchers and clinicians, but it is incremental as it builds on existing methods like principal component analysis.
The authors tackled the challenge of analyzing clinical questionnaire data with missing fields and mixed scales by proposing a latent factor modeling framework that extends principal component analysis for categorical and quantitative data, and demonstrated its application in Irritable Bowel Syndrome symptoms by finding correlations with standardized scales.
The analysis of clinical questionnaire data comes with many inherent challenges. These challenges include the handling of data with missing fields, as well as the overall interpretation of a dataset with many fields of different scales and forms. While numerous methods have been developed to address these challenges, they are often not robust, statistically sound, or easily interpretable. Here, we propose a latent factor modeling framework that extends the principal component analysis for both categorical and quantitative data with missing elements. The model simultaneously provides the principal components (basis) and each patients' projections on these bases in a latent space. We show an application of our modeling framework through Irritable Bowel Syndrome (IBS) symptoms, where we find correlations between these projections and other standardized patient symptom scales. This latent factor model can be easily applied to different clinical questionnaire datasets for clustering analysis and interpretable inference.