Longitudinal Modeling Approaches to Assess the Association Between Changes in 2 Clinical Outcome Assessments

Abstract

Background:

Understanding how one clinical outcome assessment (COA) (eg, a patient-reported outcome [PRO]) relates to a second COA (eg, a clinician-reported outcome [ClinRO]) may provide insights into disease burden or treatment efficacy. We aimed to briefly review commonly used cross-sectional methods to evaluate the association between a PRO and a ClinRO and to demonstrate the advantages of longitudinal modeling approaches, particularly a joint mixed model for repeated measures (MMRM), to evaluate this association.

Methods:

We generated an example longitudinal data set that included a PRO measured on an 11-point numeric rating scale and a binary ClinRO. The association between change in PRO score and ClinRO response at each time point was examined using 2 cross-sectional analyses: point biserial correlation and logistic regression. We conducted longitudinal analyses of the association between the 2 COAs across time points using MMRM and joint MMRM approaches.

Results:

Point-biserial correlation and logistic regression analyses correctly captured the “built in” associations between the 2 COAs that strengthened over time, but each association was applicable only for a single time point. The MMRM approach provided correlations over time but only for a single outcome variable. The joint MMRM approach modeled the relationship between both outcome variables simultaneously, allowing for evaluation of the correlations both within and between the variables over time.

Conclusion:

Each analysis demonstrated the relationship between PRO score changes and ClinRO response. Longitudinal analysis methods, particularly the joint MMRM, allow for a more thorough examination of the correlations among the 2 outcomes than cross-sectional analysis methods.