结构嵌套模型与G-估计：部分实现的承诺
Structural Nested Models and G-estimation: The Partially Realized Promise
摘要：
结构嵌套模型（SNM）和相关的G-估计方法最早由James Robins在二十多年前提出，作为建模和估计一系列治疗或暴露的联合效应的方法。这些模型和估计方法已被扩展到处理更广泛的一系列问题，并且比为估计这种联合效应而开发的其他方法具有相当大的优势。尽管有这些优点，但这些方法在应用研究中的应用相对较少;我们认为这是不幸的。为了解决这个问题，我们提供了一个概述的模型和估计方法，主要是由罗宾斯，多年来。我们提供了深入了解他们的优势比其他方法，并考虑一些可能的原因失败的方法被更广泛地采用，以及可能的补救措施。最后，我们考虑几个扩展的标准模型和估计方法。
英文摘要：
Structural nested models (SNMs) and the associated method of G-estimation were first proposed by James Robins over two decades ago as approaches to modeling and estimating the joint effects of a sequence of treatments or exposures. The models and estimation methods have since been extended to dealing with a broader series of problems, and have considerable advantages over the other methods developed for estimating such joint effects. Despite these advantages, the application of these methods in applied research has been relatively infrequent; we view this as unfortunate. To remedy this, we provide an overview of the models and estimation methods as developed, primarily by Robins, over the years. We provide insight into their advantages over other methods, and consider some possible reasons for failure of the methods to be more broadly adopted, as well as possible remedies. Finally, we consider several extensions of the standard models and estimation methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
