Investigating the causal effects of multiple treatments using longitudinal data: a simulation study
arxiv(2024)
摘要
Many clinical questions involve estimating the effects of multiple treatments
using observational data. When using longitudinal data, the interest is often
in the effect of treatment strategies that involve sustaining treatment over
time. This requires causal inference methods appropriate for handling multiple
treatments and time-dependent confounding. Robins Generalised methods
(g-methods) are a family of methods which can deal with time-dependent
confounding and some of these have been extended to situations with multiple
treatments, although there are currently no studies comparing different methods
in this setting. We show how five g-methods (inverse-probability-of-treatment
weighted estimation of marginal structural models, g-formula, g-estimation,
censoring and weighting, and a sequential trials approach) can be extended to
situations with multiple treatments, compare their performances in a simulation
study, and demonstrate their application with an example using data from the UK
CF Registry.
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