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Causal Inference in Repeated Observational Studies

重复观察研究中的因果推断

基本信息

批准号：
8267023
负责人：
Bo Lu
金额：
$ 7.44万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-06-01 至 2014-05-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): A major goal of many empirical studies in the health sciences is to evaluate the effect of treatments or policy changes. Frequently, random allocation of participants to treatments is not feasible due to practical and ethical reasons. Therefore, participants who choose a treatment may differ from those who choose the control condition. Lack of adequate controls for treated participants often leads to biased treatment effect estimation. Our proposed research is motivated by a repeated cross-sectional observational study on smoking cessation. The smoking cessation program has enrolled smokers every year since 2001 and participants voluntarily choose one of the two intervention arms. In January 2005, an indoor smoking ban was enacted in Italy, so the post-ban intervention effect is likely to be intertwined with the ban effect. Separating the effect due to this policy change from the intervention effect is of great interest to the scientific community. Several challenges are present in the analysis: 1) the program is repeated over time, thus participants are not only incomparable between different treatment arms, but also incomparable before and after the smoking ban. The analytical approach must take the time domain into consideration. 2) The unmeasured confounding is even a bigger issue in repeated observational studies, since it may influence participants' selection differently at different time points. 3) Some important outcomes, such as consumed cigarettes per day (CPD), have highly right-skewed distribution with a non-trivial portion of zeros. Thus standard regression approaches are not applicable and a distribution-free inference is desirable. Propensity score methodology is a popular approach to estimating a causal effect in observational studies. For cross-sectional data, matching or stratification based on propensity score can be used to balance the covariates distribution (Rosenbaum and Rubin, 1983). In longitudinal data, regression analysis incorporating propensity score weights is used to remove time-varying confounding provided all relevant confounders have been observed (Robins, et al. 2000). However, for repeated cross-sectional observational studies, little work has been published to address causal relationship. This project is an attempt to fill this gap by identifying assumptions for causal inference in repeated cross-sectional observational studies and establishing a new propensity score matching methodology to facilitate the estimation. The proposed propensity score matching estimators will be unbiased, distribution-free, and adapt to unknown time effects. Specifically, we plan to achieve two goals in this project: 1) Establishing a generalized potential outcome framework and extending the standard propensity score matching method to develop a difference-in-difference type of estimator for estimating the smoking cessation intervention effect, the policy change effect and their potential interaction. 2) Assessing the potential impact of unmeasured time-dependent covariates on the treatment effect estimate over time.

描述（由申请人提供）：健康科学中许多实证研究的主要目标是评估治疗或政策变化的影响。由于实际和伦理方面的原因，通常将参与者随机分配到治疗中是不可行的。因此，选择治疗的参与者可能与选择控制条件的参与者不同。缺乏足够的控制，治疗参与者往往会导致偏倚的治疗效果估计。我们提出的研究是由一个重复的横断面观察研究戒烟的动机。自2001年以来，戒烟计划每年都招募吸烟者，参与者自愿选择两种干预措施之一。2005年1月，意大利颁布了室内禁烟令，因此禁烟后的干预效果很可能与禁烟效果交织在一起。科学界对将这种政策变化的影响与干预影响分开非常感兴趣。分析中存在几个挑战：1）该计划随着时间的推移而重复，因此参与者不仅在不同治疗组之间不可比，而且在禁烟前后也不可比。分析方法必须考虑时域。2)在重复的观察性研究中，未测量的混杂因素甚至是一个更大的问题，因为它可能会在不同的时间点对参与者的选择产生不同的影响。3)一些重要的结果，如每天消耗的香烟（CPD），具有高度右偏的分布，具有非平凡的零部分。因此，标准的回归方法是不适用的，一个免费的分布推断是可取的。在观察性研究中，倾向评分法是一种常用的估计因果效应的方法。对于横断面数据，可使用基于倾向评分的匹配或分层来平衡协变量分布（Rosenbaum和Rubin，1983）。在纵向数据中，如果已观察到所有相关混杂因素，则使用结合倾向评分权重的回归分析来去除随时间变化的混杂因素（Robins等人，2000）。然而，对于重复的横断面观察性研究，几乎没有发表任何工作来解决因果关系。本项目试图通过在重复的横断面观察性研究中确定因果推断的假设，并建立一种新的倾向评分匹配方法来促进估计，从而填补这一空白。建议的倾向得分匹配估计将是无偏的，分布自由，并适应未知的时间效应。具体而言，我们计划在这个项目中实现两个目标：1）建立一个广义的潜在结果框架，并扩展标准倾向评分匹配方法，以开发一个差异中差异类型的估计器，用于估计戒烟干预效果，政策变化效果及其潜在的相互作用。2)评估未测量的时间依赖性协变量随时间推移对治疗效应估计值的潜在影响。