Regression with Latent Variables (MIMIC Models): A Better Way to Analyze Composite Scores from Instruments in Clinical Trials and Medical Research

• Activity Number: 531
• Type: Roundtables
• Date/Time: Wednesday, August 3, 2011 : 12:30 PM to 1:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Chengwu Yang*+ and Barbara C. Tilley and Anbesaw Selassie and Ruth Greene
• Companies: Penn State University and The University of Texas Health Science Center at Houston and Medical University of South Carolina and Johnson C. Smith University
• Keywords: Latent Variables ; Subjective Outcomes ; Patient-Reported Outcomes (PRO) ; Multiple-indicator Multiple-causes (MIMIC) Models ; Differential Item Functioning (DIF) ; Structural Equation Modeling (SEM)

Abstract

Subjective outcomes (e.g., Quality of Life) are prolifically used in clinical trials and medical research, and they are measured by composite scores from instruments (e.g, SF-36). The instruments' validity is a big concern, and an important validity issues is if the instrument's factor structure is sustained. The conventional multivariate regression that directly regresses the composite scores on important covariates cannot investigate the factor structure, and therefore the results can be wrong. The multiple-indicator multiple-causes (MIMIC) model that regresses the latent domain scores on the same important covariates has these advantages: 1) it can assess factor structure of an instrument; 2).it can investigate if any covariate effect on the composite scores is contaminated by measurement bias, i.e., differential item functioning (DIF). Three situations exit when applying MIMIC models to composite scores: 1) the instrument's factor structure sustained and there is no DIF; 2) the instrument's factor structure sustained but there is DIF; 3) the instrument's factor structure didn't sustain. Appropriate analyzes strategies corresponding to these three conditions will be discussed.