Statistical methods for regression modeling of global percentile outcome in neurological diseases

项目总结

PROJECT SUMMARY Design and analysis of neurological and stroke studies have been challenged by the lack of a single primary outcome that can comprehensively assess the multidimensional impairments and symptoms associated with the disease. For example, it is known that individual outcome measures for Parkinson's disease (PD), even the MDS-UPDRS, cannot comprehensively capture the full spectrum of PD signs and symptoms. The global per- centile outcome offers an efficient and stable way to integrate multiple individual outcomes, providing a single metric of the global disease severity. The O'Brien's global rank-sum test allows two or K-group comparisons for the global percentile outcome and has been successfully applied in many clinical trials, including the Neuropro- tection Exploratory Trials in Parkinson's Disease (NET-PD) Long-term Study 1 (LS-1) and FS-ZONE. However, rigorous statistical tools have been lacking for regression modeling of the global percentile outcome, preventing systematic explorations of risk factors for global disease burden and global disease progression. Motivated by these challenges and opportunities, (Aim 1) we propose a novel and rigorous regression framework to explicitly link the global percentile outcome to multiple risk factors, under minimal modeling assumptions regarding the link function and the error distribution. Our estimation procedure exploits information in the ranks to achieve robust estimation, yielding a risk score that is in maximum concordance with global disease severity. Next, (Aim 2) we will develop a sensible regression framework for exploring the time-trend of the global percentile outcome with longitudinal data, to specifically detect risk factors that lead to accelerated progression in global ranks. We further extend our methods to accommodate the common dropout mechanisms of missing completely at random and missing at random. Furthermore, (Aim 3) we will apply the proposed methods to systematically an- alyze risk factors of global disease severity and global disease progression in the LS-1 study and the FS-ZONE study. Our methods bear substantial practical utility for researchers in neurological diseases and many other fields. We will provide user-friendly software for all statistical tools to the general research community.