A tutorial on structural equation modeling for analysis of overlapping symptoms in co-occurring conditions using...

Area of Science:

• Psychometrics
• Biostatistics
• Clinical Research Methodology

Background:

• Symptom overlap between co-occurring conditions complicates accurate diagnosis and treatment evaluation.
• Patient-reported outcomes introduce systematic error due to individual interpretation, further biasing results.
• Existing methods struggle to disentangle shared symptoms and measurement error in complex clinical data.

Purpose of the Study:

• To provide a tutorial on Structural Equation Modeling (SEM) for analyzing data with measurement error and complex causal relationships.
• To demonstrate SEM's utility in addressing symptom overlap in co-occurring conditions, using depression in multiple sclerosis as a case example.
• To guide researchers and clinicians in applying SEM using MPlus software for improved diagnostic and treatment outcome assessment.

Main Methods:

• Description of SEM, including exploratory and confirmatory factor analysis, and multiple indicator multiple cause modeling.
• Application of SEM to disentangle overlapping depressive symptoms from fatigue, cognitive, and functional impairments in multiple sclerosis patients.
• Detailed guidance on using MPlus software, including data requirements, code, and output interpretation for SEM analysis.

Main Results:

• SEM offers flexibility in handling differential item functioning and disentangling symptom overlap.
• Revised scales and scoring approaches, informed by SEM, can eliminate overlap bias.
• The tutorial demonstrates a practical approach to improving diagnostic accuracy and treatment effectiveness inferences.

Conclusions:

• Structural equation modeling is a powerful tool for managing complex data challenges in clinical research.
• By disentangling symptom overlap, SEM enhances the validity of diagnostic criteria and treatment outcome measures.
• This tutorial provides a practical framework for applying SEM to improve patient care in the presence of co-occurring conditions.