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

使用辅助信息纠正 AD 风险评估以及认知和移动能力下降的偏差

• 批准号: 9374189
• 负责人: Cuiling Wang
• 金额: $ 25.05万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE, INC
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9374189
• 关键词: Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease risk; Clinical Trials; Clinical Trials Design; Cognitive; Collection; Data; Dementia; Disease; Drops; Elderly; Evaluation; Floor; Goals; Health; Impaired cognition; Incidence; Joints; Longitudinal Studies; Measures; Memory; Modeling; Observational Study; Oral; Outcome; Participant; Pattern; Performance; Persons; Population; Process; Public Health; Questionnaires; Research; Research Design; Risk Estimate; Risk Factors; Source; Specific qualifier value; Statistical Data Interpretation; Telephone; Testing; Time; Visit; Work; base; cognitive performance; cognitive testing; cohort; disorder risk; executive function; follow-up; improved; instrument; interest; longitudinal analysis; longitudinal design; simulation; walking speed

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.

摘要 评估阿尔茨海默病（AD）的风险以及认知和移动的能力下降率 性能和可能的风险因素是许多纵向老化研究的重要目标， AD和认知/移动的衰退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行为能力变化的有偏估计， 移动的性能和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。结果可能会得出误导性的科学结论。 辅助数据，与结果相关的测量，使我们能够评估随机 缺失假设，并消除或减少非随机缺失数据的偏倚。我们有 表明辅助数据有可能纠正信息缺失引起的偏差 前期工作数据。但是，辅助数据和缺失数据如何处理的详细信息 利用辅助信息影响结果仍不清楚，其他来源 包括上限和下限效应的不完整数据进一步使问题复杂化。研究 利用辅助信息来处理对AD和痴呆时间的非随机审查， 也缺乏。在这项研究中，我们计划评估辅助数据和缺失数据对 疾病风险的估计和纵向结果的变化，通过广泛的 模拟研究，并应用于AD的发病率和认知功能的下降， 移动的性能。该项目将有助于更好地了解和 辅助数据的应用，这有可能提供更好的方法来处理 使用所有可用信息缺失数据，改进临床试验的研究设计， 对衰老的观察研究