Correction of Bias in Estimating Risk of AD and Cognitive and Mobile Decline Using Auxiliary Information

摘要 评估阿尔茨海默病(AD)的风险以及认知和行动能力下降的速度 性能和可能的风险因素是许多纵向老化研究的重要目标，例如 广告和认知/移动能力下降是具有重大意义的全球公共卫生问题。一个 许多纵向老化研究面临的重要挑战是非随机缺失数据。 健康状况较差的参与者可能更有可能退出或错过一次访问，因此错过 数据可能不是随机的。这可能会导致对认知和认知能力变化的偏见估计 移动性能和AD风险，因为所有统计分析都基于以下假设 数据是随机丢失的。因此，可能会得出误导性的科学结论。 辅助数据，即与结果相关的测量，允许我们评估随机 缺失假设，并消除或减少非随机缺失数据的偏差。我们有 显示辅助数据有可能纠正由信息性缺失引起的偏差 初步工作中的数据。然而，辅助数据和缺失数据如何处理的详细信息 影响利用辅助信息的结果仍不清楚，以及其他来源的 包括天花板和地板效应在内的不完整数据使问题进一步复杂化。研究与实践 利用辅助信息处理AD和痴呆的时间的非随机审查是 同样缺乏。在这项研究中，我们计划评估辅助数据和缺失数据对 对疾病风险和纵向结果变化的估计，通过广泛的 模拟研究，并应用于阿尔茨海默病的发病和认知功能的下降 在老龄化队列中的移动性能。这个项目将导致更好的理解和 应用辅助数据，这有可能提供更好的处理方法 使用所有可用信息和改进的临床试验和研究设计的缺失数据 衰老中的观察研究。

Abstract Evaluating risk of Alzheimers' disease (AD) and rate of decline in cognitive and mobile performance and possible risk factors is an important goal in many longitudinal aging studies as AD and cognitive/mobile decline are global public health problems of enormous significance. An important challenge facing many longitudinal aging studies is non-random missing data. Participants with poorer health may be more likely to drop out or miss a visit, therefore missing data is likely not at random. This can result in biased estimates of change in cognitive and mobile performance and risk of AD, as all statistical analyses are based on the assumption that the data is missing at random. Misleading scientific conclusions can be obtained as a result. Auxiliary data, measures that are associated with the outcome, allow us to evaluate the random missing assumption and to eliminate or reduce bias from non-random missing data. We have shown that auxiliary data have the potential to correct for the bias caused by informative missing data in preliminary works. However, details of how the auxiliary data and missing data process affect the results from utilizing auxiliary information remain unclear, and other sources of incomplete data including ceiling and floor effects further complicate the problem. Research on utilizing auxiliary information to handle non-random censoring for time to AD and dementia is also lacking. In this study we plan to evaluate the impact of auxiliary data and missing data on the estimation of risk of disease and change in longitudinal outcomes, through extensive simulation studies, and with application to incidence of AD and the decline of cognitive and mobile performance in aging cohorts. This project will result in better understanding and application of auxiliary data, which has the potential to provide better approach to handle missing data using all available information, and improved study designs of clinical trials and observation studies in aging.