Over the last decade, J. M. Robins has developed a set of tools for assessing, from observational data, the causal effects of a time-dependent treatment or exposure in the presence of time-dependent covariates that may be simultaneously confounders and intermediate variables. This report concerns a case study of the application of one these techniques, G-estimation using structural nested failure time models, to the problem of assessing the effect of graft versus host disease on leukemia relapse after bone marrow transplantation.