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.Comment: Published in at http://dx.doi.org/10.1214/14-STS493 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
