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Unpublished Paper
tmle: An R Package for Targeted Maximum Likelihood Estimation
U.C. Berkeley Division of Biostatistics Working Paper Series
  • Susan Gruber, Harvard School of Public Health
  • Mark J. van der Laan, University of California - Berkeley
Date of this Version

This paper was revised May 29, 2012.

Targeted maximum likelihood estimation (TMLE) presents an approach for construction of an efficient double-robust semi-parametric substitution estimator of a target feature of the data generating distribution, such as a statistical association measure or a causal effect parameter. tmle is a recently developed R package that implements TMLE for estimation of the effect of a binary treatment at a single point in time on an outcome of interest, controlling for user supplied covariates: the additive treatment effect, the relative risk, the odds ratio. The package allows outcome data with missingness, and experimental units that contribute repeated records of the point-treatment data structure, thereby allowing this package to analyze longitudinal data structures. The TMLE of the direct effect of the binary treatment, controlling for a binary intermediate variable on the pathway from treatment to the outcome, is also implemented. Estimation of the parameters of a marginal structural model for binary treatments is also provided. Relevant factors of the likelihood may be modeled or fit by user-specified commands, or fit data-adaptively internally. Effect estimates, variances, p-values, and 95% confidence intervals are provided by the software.
Citation Information
Susan Gruber and Mark J. van der Laan. "tmle: An R Package for Targeted Maximum Likelihood Estimation" (2011)
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