Causal Inference in Repeated Observational Studies

描述(申请人提供)：健康科学中许多经验性研究的一个主要目标是评估治疗或政策变化的效果。通常，由于实际和伦理原因，随机分配参与者接受治疗是不可行的。因此，选择治疗的参与者可能与选择对照条件的参与者不同。对接受治疗的参与者缺乏足够的控制往往会导致对治疗效果的偏颇估计。我们提出的研究是由一项关于戒烟的重复横断面观察性研究推动的。自2001年以来，戒烟计划每年都会招募吸烟者，参与者自愿选择两种干预措施中的一种。2005年1月，意大利颁布了室内禁烟令，因此禁令后的干预效果很可能与禁令效果交织在一起。将这一政策变化的效果与干预效果分开是科学界非常感兴趣的事情。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的治疗武器之间是不可比较的，而且在禁烟前后也是不可比较的。分析方法必须考虑到时间域。2)在重复的观察性研究中，未测量的混淆甚至是一个更大的问题，因为它可能会在不同的时间点影响参与者的选择。3)一些重要的结果，如每天消费的香烟(CPD)，具有高度右倾的分布，具有不平凡的零部分。因此，标准的回归方法是不适用的，需要一种无分布的推论。倾向评分法是观察性研究中估计因果效应的一种流行方法。对于横断面数据，可以使用基于倾向分数的匹配或分层来平衡协变量的分布(Rosenbaum和Rubin，1983)。在纵向数据中，在观察到所有相关混杂因素的情况下，结合倾向性分数权重的回归分析被用于消除随时间变化的混杂(Robins等人。2000)。然而，对于重复的横断面观察研究，几乎没有发表过解决因果关系的工作。这个项目试图通过在重复的横断面观察研究中确定因果推断的假设，并建立一种新的倾向得分匹配方法来促进估计，以填补这一空白。所提出的倾向得分匹配估计器将是无偏的，无分布的，并适应未知的时间效应。具体地说，我们计划在这个项目中实现两个目标：1)建立一个概化的潜在结果框架，并扩展标准倾向得分匹配方法，以开发一种差异型估计器来估计戒烟干预效果、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协变量对随时间推移的治疗效果估计的潜在影响。

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.