Causal Inference in Repeated Observational Studies

重复观察研究中的因果推断

• 批准号: 8031063
• 负责人: Bo Lu
• 金额: $ 7.45万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8031063
• 关键词: Accounting; Address; Cause of Death; Cigarette; Communities; Confounding Factors (Epidemiology); Data; Enrollment; Equilibrium; Ethics; Evaluation; Goals; Health Sciences; Intervention; Italy; Methodology; Methods; Observational Study; Outcome; Participant; Policies; Population; Probability; Public Health; Publishing; Random Allocation; Regression Analysis; Reporting; Research; Risk Factors; Scoring Method; Smoker; Smoking; Smoking Cessation Intervention; Stratification; Time; Tobacco; Weight; Work; arm; base; interest; intervention effect; intervention program; programs; smoking cessation; smoking prevalence; treatment effect

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. PUBLIC HEALTH RELEVANCE: The proposed research will develop a new statistical methodology to evaluate intervention effects in repeated cross-sectional observational studies. Many public health programs are observational, in which random allocation of participants to different intervention arms is not feasible or ethical. The project will address a key methodology gap by providing a robust estimation strategy for the situation when the public health intervention program is repeated over time. We will apply the method to evaluate a smoking cessation program and elucidate the potential interaction between the treatment effect and a smoking ban effect.

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。通常，由于实际和道德原因，将参与者随机分配给治疗是不可行的。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。缺乏对治疗参与者的足够控制通常会导致偏见的治疗效果估计。我们提出的研究是由一再进行戒烟的横断面观察研究的动机。自2001年以来，戒烟计划每年都会招募吸烟者，并且参与者自愿选择了两个干预组之一。 2005年1月，意大利颁布了室内吸烟禁令，因此，在禁令效应中，木制后干预效果可能会交织在一起。将由于这种政策变化与干预效应所产生的效果分开是科学界引起的。分析中存在一些挑战：1）随着时间的推移，该程序会重复进行，因此参与者不仅在不同的治疗组之间无与伦比，而且在禁酒禁令之前和之后也无与伦比。分析方法必须考虑时间域。 2）在反复观察研究中，未衡量的混杂甚至是一个更大的问题，因为它可能在不同时间点对参与者的选择有所不同。 3）一些重要的结果，例如每天消耗的香烟（CPD），具有高度右手的分布，而零部分的零部分。因此，标准回归方法不适用，并且需要无分配推断。倾向得分方法是一种估计观察性研究因果效应的流行方法。对于横截面数据，可以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，只要观察到所有相关的混杂因素，纵向评分权重的回归分析都用于消除时变的混杂（Robins等，2000）。但是，对于反复的横断面观测研究，很少发表工作来解决因果关系。该项目是通过确定重复的横截面观察性研究中因果推断的假设来填补这一空白的一种尝试，并建立了新的倾向得分匹配方法以促进估计。所提出的倾向分数匹配估计器将是公正的，无分布的，并适应未知的时间效应。具体而言，我们计划在该项目中实现两个目标：1）建立广义的潜在结果框架并扩展标准倾向分数匹配方法，以开发估计估计限制估计量的差异差异类型，以估计戒烟干预效果，策略变化效果及其潜在的相互作用。 2）评估未衡量的时间依赖性协变量对随着时间的流逝效果估计的潜在影响。 公共卫生相关性：拟议的研究将开发一种新的统计方法，以评估反复的横断观察研究中的干预效果。许多公共卫生计划都是观察性的，其中参与者将参与者随机分配给不同的干预武器是不可行或道德的。该项目将通过为随着时间的推移重复公共卫生干预计划提供强大的估计策略来解决关键方法论差距。我们将采用该方法来评估戒烟计划，并阐明治疗效果与吸烟禁令效应之间的潜在相互作用。