Using propensity scores for causal inference with covariate measurement error

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？“或“在什么条件下有效？“这些问题通常只能在非实验性研究的背景下回答。倾向分数是非实验研究的一个关键统计工具，因为它们有助于“苹果与苹果”的比较。“然而，许多非实验性研究需要结合来自多个数据集的信息，这些数据集可能具有略微不同的测量方法。例如，使用两种不同的量表测量抑郁症，或者直接与父母报告的自闭症儿童行为测量。现有的倾向评分方法无法处理协变量测量错误或在治疗组和对照组之间测量不同的情况。这在心理健康研究中是一个特别的挑战，其中许多研究中的疾病和因素是不可直接观察的，而是作为潜在的结构建模。该项目将开发和评估新的统计方法，用于估计非实验研究中的治疗效果，当协变量测量错误或以不同的方式测量治疗组和对照组时。这项工作将结合在一起的倾向评分方法估计治疗效果，潜在变量的方法，和多重插补方法处理缺失数据。其目标是：1）当使用倾向评分法估计治疗效果时，调查协变量中测量误差的含义，2）针对治疗组和对照组中某些协变量测量误差或测量不同的情况，开发和评估倾向评分法，3）使用目标1和2的结果和方法估计三项研究的治疗效果，每一组都将接受干预的一组与外部对照组进行比较，然后通过将这些估计值与相同干预措施的随机试验报告的治疗效果进行比较来测试这些方法。这些方法将在三项心理健康研究的背景下进行检查，评估（1）自闭症儿童的早期干预，（2）围产期抑郁症的预防，以及（3）使用银杏叶预防痴呆症和阿尔茨海默病的有效性。

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.