ESTIMATING CAUSAL EFFECTS IN LONGITUDINAL STUDIES

估计纵向研究中的因果效应

• 批准号: 6125847
• 负责人: Marshall M Joffe
• 金额: $ 11.14万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-12-15 至 2002-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6125847
• 关键词: bias; computer data analysis; data collection methodology /evaluation; data management; human data; longitudinal human study; method development; outcomes research; statistics /biometry; therapy compliance

(Adapted from Investigator's Abstract) Time-varying exposures and treatments are ubiquitous in epidemiology and medicine. The primary goal of many studies is to determine the joint causal effects of exposures received at different times on some outcome of interest. In recent years, Robins and others developed comprehensive approaches for defining and estimating such causal effects. This formal framework provides insight into the analysis and interpretation of experimental and observational data in general and into dealing with confounding by a variable affected by the study exposure in particular, a problem which can manifest itself in both observational and randomized studies. The application seeks to extend these insights and simplify their application. Regression, broadly understood, is the most common way to analyze data on time-varying exposures in observational studies, and includes methods for dealing with the time varying covariates and correlated outcomes typical in longitudinal studies. In the presence of confounding by a variable affected by exposure regression will sometimes produce misleading or biased estimates on the joint causal effects of exposure received at different times. Nonetheless, there has been little attempt to quantify the extent of the bias. The application's first aim is to quantify the bias in these familiar methods and to learn when it is valid to assign causal interpretation to regression estimates. In the presence of confounding by a variable affected by exposure, Robins' G-estimation approach provides an alternative to familiar methods for analyzing randomized and observational studies which adopts attractive features of both. Nonetheless, it has been little used in applications. The application's second goal is to simplify the application of this approach by developing easy-to-use estimators that are reasonably efficient in common situations and making available software to perform these analyses. Causal interpretation of exposure-outcome associations in observational studies depends on untestable assumptions. The usual conduct of observational studies of time-varying exposures makes some of the commonly made assumptions suspect. The G-estimation approach allows valid estimation of exposure effect under less restrictive and more flexible assumptions. The application's third goal is to extend earlier work showing how to use G-estimation to obtain valid estimates under these more flexible and better justified assumptions.

(改编自《调查者摘要》)时变曝光和 在流行病学和医学中，治疗方法无处不在。首要目标 许多研究的目的是确定暴露的联合因果关系 在不同的时间收到的一些感兴趣的结果。近年来， Robins和其他人开发了全面的方法来定义和 估计这样的因果影响。这个正式的框架提供了洞察力 进入对实验和观测的分析和解释 一般数据和处理受变量影响的混杂问题 特别是通过研究暴露，一个可以表现出来的问题 在观察性和随机性研究中。该应用程序寻求 扩展这些洞察力并简化其应用。 广义上讲，回归是分析数据的最常见的方法 观测研究中的时变曝光，并包括用于 处理典型的时变协变量和相关结果 在纵向研究中。在存在被变量混淆的情况下 受暴露影响回归有时会产生误导性或 对在以下地点收到的接触的联合因果影响的偏颇估计 不同的时代。尽管如此，几乎没有人试图量化 偏见的程度。该应用程序的第一个目标是量化 对这些熟悉的方法有偏见，并了解何时分配是有效的 回归估计的因果解释。 在存在受暴露影响的变量的混杂情况下，罗宾斯的 G-估计方法提供了一种替代常见方法的方法 分析采用有吸引力的随机和观察性研究 两者的特点。尽管如此，它在应用中却很少使用。 该应用程序的第二个目标是简化此应用程序 通过开发易于使用的估计器来实现 在常见情况下高效，并使软件可用于执行 这些分析。 观察性暴露-结果关联的因果解释 研究依赖于不可检验的假设。……的通常行为 对时变曝光的观测研究得出了一些常见的 做出了令人怀疑的假设。G-估计方法允许有效 在限制较少和较灵活的情况下的暴露效应估计 假设。应用程序的第三个目标是扩展早期的工作 展示如何使用G-估计在这些更多的情况下获得有效的估计 更灵活、更合理的假设。