Latent Factor Decomposition Model: Applications for Questionnaire Data

TL;DR

This paper introduces a robust latent factor model extending PCA to handle mixed-type clinical questionnaire data with missing values, enabling better interpretation and clustering of patient symptoms.

Contribution

The paper presents a novel latent factor modeling framework that extends PCA for mixed data types with missing values, improving interpretability and applicability in clinical datasets.

Findings

Identified correlations between latent projections and symptom scales in IBS data.

Demonstrated the model's ability to handle missing data and mixed data types.

Showed potential for clustering and interpretability in clinical questionnaires.

Abstract

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.