Targeted maximum likelihood estimation for a binary treatment: A tutorial.

ABSTRACT: When estimating the average effect of a binary treatment (or exposure) on an outcome, methods that incorporate propensity scores, the G-formula, or targeted maximum likelihood estimation (TMLE) are preferred over naïve regression approaches, which are biased under misspecification of a parametric outcome model. In contrast propensity score methods require the correct specification of an exposure model. Double-robust methods only require correct specification of either the outcome or the exposure model. Targeted maximum likelihood estimation is a semiparametric double-robust method that improves the chances of correct model specification by allowing for flexible estimation using (nonparametric) machine-learning methods. It therefore requires weaker assumptions than its competitors. We provi

SUBMITTER: Luque-Fernandez MA 

PROVIDER: S-EPMC6032875 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature