ESTIMATING CAUSAL EFFECTS IN LONGITUDINAL STUDIES

（改编自研究者摘要）时变暴露量和 治疗在流行病学和医学中无处不在。首要目标 许多研究的目的是确定暴露的联合因果效应 在不同的时间收到一些利益的结果。近年来， 罗宾斯和其他人开发了全面的方法来定义和 估计这种因果效应。这个正式的框架提供了洞察力 分析和解释实验和观测 一般数据和处理受影响变量的混淆 特别是通过研究暴露，一个问题可以表现出来， 在观察性和随机研究中。该申请旨在 扩展这些见解并简化其应用。 回归，广义上理解，是分析数据的最常见方法， 观察性研究中的时变暴露，并包括 处理随时间变化的协变量和相关结果， 在纵向研究中。在存在变量混杂的情况下 受曝光回归的影响，有时会产生误导或 在以下时间收到的暴露联合因果效应的有偏估计： 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 在这些熟悉的方法中的偏见，并学习什么时候分配是有效的， 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， G-估计方法提供了一种替代熟悉的方法， 分析随机和观察性研究， 两者的特点。然而，它在应用中很少使用。 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算方法， 在常见情况下有效，并提供软件来执行 这些分析。 观察性研究中确定性-结果关联的因果解释 研究依赖于无法验证的假设。的通常行为 对随时间变化的暴露的观察性研究使一些常见的 让人怀疑的假设G估计方法允许有效的 在更少限制和更灵活的条件下估计暴露效应 假设。该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-估计来获得这些更有效的估计 灵活且更合理的假设。

(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.