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Estimating the causal effect of a time‐varying treatment on time‐to‐event using structural nested failure time models
Author(s) -
Lok Judith,
Gill Richard,
Van Der Vaart Aad,
Robins James
Publication year - 2004
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2004.00123.x
Subject(s) - confounding , event (particle physics) , marginal structural model , observational study , nested set model , class (philosophy) , econometrics , computer science , mathematics , statistics , data mining , artificial intelligence , physics , quantum mechanics , relational database
In this paper we review an approach to estimating the causal effect of a time‐varying treatment on time to some event of interest. This approach is designed for a situation where the treatment may have been repeatedly adapted to patient characteristics, which themselves may also be time‐dependent. In this situation the effect of the treatment cannot simply be estimated by conditioning on the patient characteristics, as these may themselves be indicators of the treatment effect. This so‐called time‐dependent confounding is typical in observational studies. We discuss a new class of failure time models, structural nested failure time models, which can be used to estimate the causal effect of a time‐varying treatment, and present methods for estimating and testing the parameters of these models.