Identifying the clinical relative importance of each correlated outcome variables in multivariate approach: an...

In scenarios involving correlated endpoints, multivariate methods offer increased robustness for comparisons. However, understanding the individual contribution of each variable toward multivariate hypothesis rejection remains underexplored. Usually, this question is sidelined, and separate univariate analyses are performed. This paper addresses this gap by demonstrating the relative importance and contribution of variables toward the rejection of multivariate hypotheses, comparing it against a univariate approach using clinical trial data. Using the ACCORD lipid trial dataset, which includes lipid measurements of triglycerides (TG), low-density lipoprotein (LDL), and high-density lipoprotein (HDL), we employed Hotelling's T² multivariate statistic for two-group comparisons. We showcased the significance and relative importance of contributions through standardized discriminant function coefficients and partial F tests. Additionally, we investigated the impact of varying correlation levels on the significance of each variable's contribution in multivariate versus univariate approaches. Our results revealed significant lipid differences in a multivariate context at the 12th and 36th months. Across both follow-ups, TG exhibited the highest relative importance and contribution, followed by HDL and LDL. Notably, in the 36th month, the univariate approach rendered LDL's contribution insignificant for group separation, contrasting with the significant contribution identified in the multivariate approach. Furthermore, the significance likelihood of variable contributions in group separation within the multivariate approach increased with rising correlation levels. The simulation technique and the power analysis was also adopted to characterize the features of the proposed method. Our approach enables the evaluation of the relative importance and significance of each variable's contribution within the multivariate framework. This methodology holds promise for enhancing the interpretation of clinical trial analysis outcomes, particularly when dealing with multiple correlated endpoints.