Project R-15699

Covariate adjustment for multivariate outcomes (Research)

In health care, the approval of new treatments relies on clinical trials. Traditional methods focus on single endpoints, but this often insufficient to capture the full benefit in diseases, to jointly evaluate benefit-risk and to take account of patient-reported outcomes (PROs). Statistical methods for analyzing multiple outcomes have been developed, but are limited in several ways, not the least in the number and types of outcomes that can be combined. Recently, the Generalized Pairwise Comparisons (GPC) method has been suggested which addresses these concerns and has gained traction in clinical applications and even has led to drug approvals. The disadvantage of GPC is that it lacks covariate adjustment. Probabilistic Index Models (PIM) have been developed independently, but cannot deal with multivariate outcomes, nor with missing values. The objective of this project is to extend the PIM methodology to multivariate outcomes, handling missing data and extend inference to rare diseases, so as to make them useful for clinical practices. The advantages of PIMs should be investigated by comparing them to alternative methods for covariate adjustment (e.g. joint models and semiparametric ANCOVA). To facilitate the application in clinical practice, all new methods will be implemented as R packages.

01 January 2025 - 31 December 2028