Longitudinal Modeling Approaches to Assess Two Clinical Outcome Assessments

Share on: 

View abstract:

Longitudinal modeling approaches to assess the association between changes in 2 clinical outcome assessments. Odom D, McLeod L, Sherif B, Nelson L, McSorley D.

Video Transcript:

With an increased regulatory focus on incorporating patient perspectives in drug development, patient-reported outcomes, known as PROs, are playing an increasingly key role. Often an important step in helping stakeholders understand the relevance of PROs, is to demonstrate the relationship between a PRO and a more familiar clinician-reported outcome or biological marker. 

Most of the research in this area uses cross-sectional analyses that offer only an evaluation of the data at individual time points and does not account for the longitudinal design of most trials. Moreover, there is a risk that results from cross-sectional methods may be misinterpreted by examining the relationship across time-points.

Our goal for this study was to demonstrate the advantages of using longitudinal data methods – and particularly a joint mixed model for repeated measures – versus more commonly used cross-sectional methods based on an example data set consisting of a PRO and clinician-reported outcome. Our research showed that the joint MMRM approach modeled the relationship between both outcome variables simultaneously, allowing for a more thorough examination including an evaluation of the statistical significance of the correlations both within and between the outcomes over time.

We hope that this article helps researchers understand the value of using more robust analysis approaches, such as longitudinal joint MMRM, when examining relationships between different types of outcome assessments.

Review some of our longitudinal modeling studies here

Related Services:
Staff Members: