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

项目总结 神经病学和中风研究的设计和分析因缺乏单一的初级研究而受到挑战 可以全面评估与以下各项相关的多维损伤和症状的结果 这种疾病。例如，众所周知，帕金森氏病(PD)的个人结局衡量标准，即使是 MDS-UPDRS，不能全面捕捉PD的全部体征和症状。全球Per- 百分位数结果提供了一种有效且稳定的方法来集成多个单独的结果，提供了一个单一的fi 衡量全球疾病严重程度的指标。O‘Brien的全局秩和检验允许两个或K组比较 全球百分位数结果，并已成功应用于许多临床试验，包括Neuropro. 帕金森病探索性试验(NET-PD)、长期研究1(LS-1)和FS区。然而， 对于全球百分位数结果的回归建模，一直缺乏严格的统计工具，从而防止 对全球疾病负担和全球疾病进展的风险因素进行系统探讨。动机是 这些挑战和机遇，(目标1)我们提出了一个新颖而严格的回归框架，以明确 将全局百分位数结果与多个风险因素联系起来，在关于 链路函数和误差分布。我们的评估程序利用队伍中的信息来实现 稳健的估计，得出与全球疾病严重程度最一致的风险分数。接下来，(瞄准 2)我们将开发一个合理的回归框架，以探索全球百分位数结果的时间趋势 通过纵向数据，fi可以专门检测导致全球排名加速上升的风险因素。 我们进一步扩展了我们的方法，以适应常见的完全丢失的丢弃机制 随机的和随机的失踪。此外，(目标3)我们将把所建议的方法应用于系统地和- 在LS-1研究和FS区分析全球疾病严重性和全球疾病进展的风险因素 学习。我们的方法对神经系统疾病和许多其他领域的研究人员具有重要的实用价值。 fi字段。我们将向一般研究界提供所有统计工具的用户友好软件。

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.