Longitudinal Modeling Approaches to Assess the Association Between Changes in 2 Clinical Outcome Assessments
Odom, D., Mcleod, L., Sherif, B., Nelson, L., & Mcsorley, D. (2017). Longitudinal Modeling Approaches to Assess the Association Between Changes in 2 Clinical Outcome Assessments. Therapeutic Innovation & Regulatory Science, . DOI: 10.1177/2168479017731584
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.
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.
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.
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.