Super learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling.

@article{Neugebauer2013SuperLT,
  title={Super learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling.},
  author={Romain Neugebauer and Bruce H Fireman and Jason Roy and Marsha A. Raebel and Gregory A Nichols and Patrick J. O'Connor},
  journal={Journal of clinical epidemiology},
  year={2013},
  volume={66 8 Suppl},
  pages={S99-109}
}
OBJECTIVE Clinical trials are unlikely to ever be launched for many comparative effectiveness research (CER) questions. Inferences from hypothetical randomized trials may however be emulated with marginal structural modeling (MSM) using observational data, but success in adjusting for time-dependent confounding and selection bias typically relies on parametric modeling assumptions. If these assumptions are violated, inferences from MSM may be inaccurate. In this article, we motivate the… CONTINUE READING
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