Estimating long-term treatment effects in observational data: A comparison of the performance of different methods under real-world uncertainty.

ABSTRACT: In the presence of time-dependent confounding, there are several methods available to estimate treatment effects. With correctly specified models and appropriate structural assumptions, any of these methods could provide consistent effect estimates, but with real-world data, all models will be misspecified and it is difficult to know if assumptions are violated. In this paper, we investigate five methods: inverse probability weighting of marginal structural models, history-adjusted marginal structural models, sequential conditional mean models, g-computation formula, and g-estimation of structural nested models. This work is motivated by an investigation of the effects of treatments in cystic fibrosis using the UK Cystic Fibrosis Registry data focussing on two outcomes: lung function (cont

SUBMITTER: Newsome SJ 

PROVIDER: S-EPMC6001810 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature