Using generalized linear models to implement g‐estimation for survival data with time‐varying confounding

Using data from observational studies to estimate the causal effect of a time‐varying exposure, repeatedly measured over time, on an outcome of interest requires careful adjustment for confounding. Standard regression adjustment for observed time‐varying confounders is unsuitable, as it can eliminate part of the causal effect and induce bias. Inverse probability weighting, g‐computation, and g‐estimation have been proposed as being more suitable methods. G‐estimation has some advantages over the other two methods, but until recently there has been a lack of flexible g‐estimation methods for a survival time outcome. The recently proposed Structural Nested Cumulative Survival Time Model (SNCSTM) is such a method. Efficient estimation of the parameters of this model required bespoke software. In this article we show how the SNCSTM can be fitted efficiently via g‐estimation using standard software for fitting generalised linear models. The ability to implement g‐estimation for a survival outcome using standard statistical software greatly increases the potential uptake of this method. We illustrate the use of this method of fitting the SNCSTM by reanalyzing data from the UK Cystic Fibrosis Registry, and provide example R code to facilitate the use of this approach by other researchers