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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.
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Keiding et al. (1999) studied this question.
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