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Article
tmle: An R package for targeted maximum likelihood estimation
Journal of Statistical Software (2012)
  • Susan Gruber
  • Mark van der Laan
Abstract

Targeted maximum likelihood estimation (TMLE) is a general approach for constructing an efficient double-robust semi-parametric substitution estimator of a causal effect parameter or statistical association measure. tmle is a recently developed R package that implements TMLE of the effect of a binary treatment at a single point in time on an outcome of interest, controlling for user supplied covariates, including an additive treatment effect, relative risk, odds ratio, and the controlled direct effect of a binary treatment controlling for a binary intermediate variable on the pathway from treatment to the outcome. Estimation of the parameters of a marginal structural model is also available. The package allows outcome data with missingness, and experimental units that contribute repeated records of the point-treatment data structure, thereby allowing the analysis of longitudinal data structures. Relevant factors of the likelihood may be modeled or fit data-adaptively according to user specifications, or passed in from an external estimation procedure. Effect estimates, variances, p values, and 95% confidence intervals are provided by the software.

Keywords
  • tmle,
  • R package,
  • software,
  • causal inference,
  • targeted maximum likelihood estimation
Publication Date
2012
Citation Information
Susan Gruber and Mark van der Laan. "tmle: An R package for targeted maximum likelihood estimation" Journal of Statistical Software Vol. 51 Iss. 13 (2012)
Available at: http://works.bepress.com/sgruber/22/