  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.
