Statistical Methods to Correct for Proxy Bias in Studies of Older Adults

纠正老年人研究中代理偏差的统计方法

• 批准号: 8111548
• 负责人: Michelle Denise Shardell
• 金额: $ 13.52万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8111548
• 关键词: Accounting; Address; Aging; Behavior; Biology; Biometry; Caregiver Burden; Caregivers; Caring; Clinical; Communities; Computer software; Cross-Sectional Studies; Data; Development Plans; Disease; Educational workshop; Effectiveness of Interventions; Elderly; Emotional; Epidemiologic Studies; Evaluation; Exposure to; Fracture; Future; Geriatrics; Gerontology; Goals; Health; Hip Fractures; Impaired cognition; Intervention; Interview; Lead; Link; Longitudinal Studies; Measurement; Measures; Mental Depression; Mentors; Methods; Mission; Outcome; Patient Care; Patients; Persons; Policies; Problem Solving; Proxy; Public Health; Quality of life; Recovery; Recruitment Activity; Regression Analysis; Relative (related person); Research; Research Design; Research Personnel; Respondent; Sampling; Selection Bias; Shadowing (Histology); Sociology; Statistical Methods; Structure; Testing; Validation; Visit; age related; career development; cohort; computer program; design; disability; improved; innovation; instrumental activity of daily living; investigator training; meetings; novel strategies; outcome forecast; programs; psychosocial; response; simulation; statistics; symposium; theories; tool; validation studies

DESCRIPTION (provided by applicant): This proposal has two goals: 1) to develop Dr. Shardell, a biostatistician, into an independent investigator performing research at the interface of biostatistics and gerontology; and 2) to develop statistical methods needed to more appropriately use data from proxy respondents (e.g., relatives or care givers) in epidemiological studies of elderly hip-fracture patients. Regarding the latter goal, a significant problem in epidemiologic studies of hip fracture patients is selection bias due to the large amount of data missing from the most frail and cognitively impaired patients, which may lead to biased results and inaccurate study conclusions. Currently, in order to minimize selection bias due to missing data, proxies are recruited to supply responses in place of patients who are unable or unwilling to respond to interview questions. However, responses from proxies may be systematically biased. This bias is a significant problem because it implies that the standard statistical approach of imputing missing patient data with responses from proxies can lead to inaccurate study conclusions. Therefore, bias from these proxies can impede investigators' ability to accurately identify promising targets of intervention that may improve patients' post-fracture prognosis. To solve this problem, statistical methods originally designed to adjust for selection bias from missing data will be extended to include proxy data. Availability of statistical methods that can correct for proxy bias can help in designing accurately-targeted interventions for postfracture recovery. Computer programs for the methods will be made available online for use by the gerontology community. The new approaches will be validated and compared as part of a future R01 proposal. The three-year mentored research program described in this proposal involves a career development plan that includes coursework in the biology, sociology, and psychosocial aspects of aging; regular meetings with mentors; participation at seminars, workshops, and professional conferences; and exposure to clinical geriatrics settings by shadowing clinician-researchers. Trained investigators with expertise in both biostatistics and gerontology are urgently needed to solve problems in study design, outcome measurement, and statistics that are relevant to aging research. PUBLIC HEALTH RELEVANCE: Accurately identifying behaviors and other targets of intervention that can improve physical and emotional functioning and protect against age-related diseases is the primary goal of gerontological research. Effective policy for care of older adults requires the ability to determine factors that can improve prognosis. Current statistical methods used to inform these decisions rely on imputing missing patient data with proxy data, methods which are known to lead to biased results. Improved statistical methods will allow correction for proxy bias to improve the accuracy of results, leading to more accurate conclusions regarding the effectiveness of interventions on health outcomes.

描述（由申请人提供）：本提案有两个目标：1）将生物统计学家Shardell博士培养为在生物统计学和老年学界面上进行研究的独立研究者; 2）开发更适当地使用代理受访者数据所需的统计方法（例如，亲属或护理人员）在老年髋部骨折患者的流行病学研究。关于后一个目标，在髋部骨折患者的流行病学研究中的一个重要问题是选择偏倚，这是由于从最虚弱和认知受损的患者中缺失了大量数据，这可能导致偏倚结果和不准确的研究结论。目前，为了最大限度地减少由于缺失数据而导致的选择偏倚，招募代理人来代替无法或不愿意回答访谈问题的患者提供答案。然而，代理人的反应可能是系统性的偏见。这种偏倚是一个重要的问题，因为它意味着用代理人的反应插补缺失患者数据的标准统计方法可能导致不准确的研究结论。因此，来自这些代理的偏差可能会阻碍研究人员准确识别可能改善患者骨折后预后的有希望的干预目标的能力。为了解决这个问题，统计方法最初设计用于调整缺失数据的选择偏差，将扩展到包括代理数据。可用的统计方法，可以纠正代理偏差，可以帮助设计准确的有针对性的干预骨折后恢复。这些方法的计算机程序将在网上提供，供老年学界使用。新方法将作为未来R 01提案的一部分进行验证和比较。本提案中描述的为期三年的指导研究计划涉及职业发展计划，其中包括生物学，社会学和老龄化的心理社会方面的课程;与导师定期会面;参加研讨会，讲习班和专业会议;并通过跟踪临床医生研究人员接触临床老年病学环境。在生物统计学和老年学方面都有专业知识的训练有素的调查人员迫切需要解决与老龄化研究相关的研究设计，结果测量和统计方面的问题。 公共卫生相关性：准确识别行为和其他干预目标，可以改善身体和情绪功能，并防止与年龄有关的疾病是老年学研究的主要目标。有效的老年人护理政策需要能够确定可以改善预后的因素。目前用于告知这些决策的统计方法依赖于用代理数据估算缺失的患者数据，已知这些方法会导致偏倚结果。改进的统计方法将能够纠正替代偏差，以提高结果的准确性，从而就干预措施对健康结果的有效性得出更准确的结论。