Using propensity scores for causal inference with covariate measurement error

使用倾向得分进行带有协变量测量误差的因果推断

• 批准号: 8690155
• 负责人: Elizabeth A. Stuart
• 金额: $ 27.6万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8690155
• 关键词: Address; Alzheimer' s Disease; Apple; Autistic Disorder; Behavior; Child; Data; Data Analyses; Data Set; Data Sources; Dementia; Disease; Early Intervention; Effectiveness; Ethics; Ginkgo biloba; Intervention; Measurement; Measures; Mental Depression; Mental Health; Methods; Modeling; National Institute of Mental Health; Parents; Performance; Perinatal; Placebos; Policies; Prevention; Public Health; Randomized; Randomized Controlled Trials; Reporting; Research; Research Personnel; Resource Sharing; Scoring Method; Statistical Methods; Target Populations; Testing; Work; base; comparative effectiveness; comparison group; depression prevention; effectiveness research; effectiveness trial; innovation; novel; prevent; programs; public health relevance; randomized trial; research study; simulation; tool; treatment effect; trial comparing

DESCRIPTION (provided by applicant): Many studies in public health, including comparative effectiveness research, aim to answer questions such as "what works for whom?" or "under what conditions does it work?" Such questions can often only be answered in the context of a non-experimental study. Propensity scores are a key statistical tool for non-experimental studies because they facilitate the comparison of "apples with apples." However, many non-experimental studies require combining information from multiple data sets, which may have slightly different measures available. Examples include depression measured using two different scales, or direct- versus parent-reported measures of behavior among children with autism. Existing propensity score methods cannot handle situations where the covariates are measured with error or are measured differently across treatment and comparison groups. This is a particular challenge in mental health research, where many of the disorders and factors under study are not directly observable and are instead modeled as latent constructs. This project will develop and assess new statistical methods for estimating treatment effects in non-experimental studies when the covariates are measured with error or are measured in different ways across the treatment and comparison groups. The work will tie together propensity score methods for estimating treatment effects, latent variable methods, and multiple imputation methods for handling missing data. The aims are: 1) Investigate the implications of measurement error in the covariates when using propensity score methods to estimate treatment effects, 2) Develop and assess propensity score methods for settings where some of the covariates are measured with error or are measured differently in the treatment and comparison groups, and 3) Use the results and methods from Aims 1 and 2 to estimate treatment effects in three studies, each comparing a group receiving the intervention to an external comparison group, and then test the methods by comparing those estimates to the reported treatment effects from randomized trials of the same interventions. The methods will be examined in the context of three studies in mental health evaluating the effectiveness of (1) early intervention for children with autism, (2) perinatal depression prevention, and (3) the use of ginkgo biloba to prevent dementia and Alzheimer's disease.

描述（由申请人提供）：许多公共卫生研究，包括比较有效性研究，旨在回答诸如“什么对谁有效？”等问题。或“它在什么条件下起作用？”这些问题通常只能在非实验研究的背景下才能得到回答。倾向得分是非实验研究的关键统计工具，因为它们有助于“同类之间”的比较。然而，许多非实验研究需要组合来自多个数据集的信息，这些数据集的可用测量值可能略有不同。例子包括使用两种不同量表测量抑郁症，或者对自闭症儿童的行为进行直接与家长报告的测量。现有的倾向评分方法无法处理协变量测量有误或在治疗组和对照组之间测量不同的情况。这是心理健康研究中的一个特殊挑战，其中许多正在研究的疾病和因素无法直接观察到，而是被建模为潜在的结构。该项目将开发和评估新的统计方法，用于在非实验研究中当协变量测量有误差或在治疗组和对照组中以不同方式测量时估计治疗效果。这项工作将把用于估计治疗效果的倾向评分方法、潜在变量方法和用于处理缺失数据的多重插补方法结合起来。目的是：1) 在使用倾向评分方法估计治疗效果时，研究协变量中测量误差的影响，2) 针对某些协变量测量有误差或在不同情况下测量不同的情况，开发和评估倾向评分方法。治疗组和对照组，以及 3) 使用目标 1 和 2 的结果和方法来估计三项研究中的治疗效果，每项研究都将接受干预的组与外部比较组进行比较，然后通过比较这些估计值来测试方法相同干预措施的随机试验报告的治疗效果。这些方法将在三项心理健康研究的背景下进行检验，评估以下方面的有效性：(1) 对自闭症儿童进行早期干预；(2) 预防围产期抑郁症；(3) 使用银杏叶预防痴呆和阿尔茨海默病。