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效果。由于实际和道德原因，随机分配参与者接受治疗通常是不可行的。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。对治疗参与者缺乏足够的控制通常会导致治疗效果估计出现偏差。我们提出的研究是由一项关于戒烟的重复横断面观察研究推动的。自 2001 年以来，戒烟计划每年都会招募吸烟者，参与者自愿选择两个干预组之一。 2005年1月，意大利颁布了室内禁烟令，因此禁令后的干预效应很可能与禁令效应交织在一起。将这一政策变化的影响与干预效果分开引起了科学界的极大兴趣。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没有可比性，而且在禁烟前后也没有可比性。分析方法必须考虑时域。 2）在重复观察研究中，无法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，例如每天消耗的香烟量 (CPD)，具有高度右偏分布，其中有很大一部分为零。因此，标准回归方法不适用，需要无分布推理。倾向评分方法是观察性研究中估计因果效应的一种流行方法。对于横截面数据，基于倾向得分的匹配或分层可用于平衡协变量分布（Rosenbaum 和 Rubin，1983）。在纵向数据中，如果所有相关的混杂因素都已被观察到，则采用结合倾向得分权重的回归分析来消除随时间变化的混杂因素（Robins 等人，2000 年）。然而，对于重复的横断面观察研究，很少有研究发表来解决因果关系。该项目试图通过在重复的横断面观察研究中确定因果推理的假设并建立新的倾向评分匹配方法来促进估计来填补这一空白。所提出的倾向得分匹配估计器将是无偏的、无分布的并且适应未知的时间效应。具体来说，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，并扩展标准倾向评分匹配方法，开发双重差分类型的估计量，用于估计戒烟干预效果、政策变化效果及其潜在相互作用。 2) 评估随着时间的推移，未测量的时间相关协变量对治疗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来评估重复横断面观察研究的干预效果。许多公共卫生项目都是观察性的，将参与者随机分配到不同的干预组是不可行或不道德的。该项目将通过为公共卫生干预计划随着时间的推移重复进行时的情况提供稳健的估计策略，解决关键的方法差距。我们将应用该方法来评估戒烟计划，并阐明治疗效果和禁烟效果之间的潜在相互作用。