The paper presents a robust rank-based longitudinal global percentile outcome and associated regression/estimation methods (including dropout handling and variance estimation) to integrate multiple time-varying endpoints and applies it to a Parkinson’s disease clinical trial.

This approach improves sensitivity and interpretability for PD trial analyses and risk-factor detection—helpful for trial design, endpoint selection, and translational prioritization—even though it does not identify biological mechanisms or direct therapeutic targets.

Many chronic diseases exhibit multifaceted symptoms that cannot be comprehensively characterized by one outcome. To address this, researchers often adopt a global outcome to combine information from multiple individual outcomes. The global rank-sum facilitates robust integration of multiple outcomes and has been applied in many clinical studies. We consider longitudinal settings and devise a global percentile outcome for depicting patients' time-varying global disease burden. We develop useful regression strategies for the longitudinal global percentile outcome based on a flexible regression framework of the monotonic index model. Posing minimal restrictions, we propose a maximum rank correlation type estimator and show that it entails desirable asymptotic properties. The methods are also extended to accommodate the common missing at random dropout scenarios. We propose a computationally stable and efficient procedure for parameter estimation, as well as a perturbation scheme for consistent variance estimation. Numerical studies show that our method performs well under realistic settings. We apply the proposed method to data from a Parkinson's disease clinical trial to examine risk factors associated with elevated global disease burden and accelerated disease progression.