We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Super learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling.
- Authors
Neugebauer, Romain; Fireman, Bruce; Roy, Jason A; Raebel, Marsha A; Nichols, Gregory A; O'Connor, Patrick J
- Abstract
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 application of a data-adaptive estimation approach called super learning (SL) to avoid reliance on arbitrary parametric assumptions in CER.
- Publication
Journal of clinical epidemiology, 2013, Vol 66, Issue 8 Suppl, pS99
- ISSN
1878-5921
- Publication type
Journal Article
- DOI
10.1016/j.jclinepi.2013.01.016