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Article
An Application of Collaborative Targeted Maximum Likelihood Estimation in Causal Inference and Genomics
The International Journal of Biostatistics (2010)
  • Susan Gruber, University of California, Berkeley
  • Mark J. van der Laan, University of California, Berkeley
Abstract
A concrete example of the collaborative double-robust targeted likelihood estimator (C-TMLE) introduced in a companion article in this issue is presented, and applied to the estimation of causal effects and variable importance parameters in genomic data. The focus is on non-parametric estimation in a point treatment data structure. Simulations illustrate the performance of C-TMLE relative to current competitors such as the augmented inverse probability of treatment weighted estimator that relies on an external non-collaborative estimator of the treatment mechanism, and inefficient estimation procedures including propensity score matching and standard inverse probability of treatment weighting. C-TMLE is also applied to the estimation of the covariate-adjusted marginal effect of individual HIV mutations on resistance to the anti-retroviral drug lopinavir. The influence curve of the C-TMLE is used to establish asymptotically valid statistical inference. The list of mutations found to have a statistically significant association with resistance is in excellent agreement with mutation scores provided by the Stanford HIVdb mutation scores database.
Keywords
  • causal effect,
  • cross-validation,
  • collaborative double robust,
  • double robust,
  • efficient influence curve,
  • penalized likelihood,
  • penalization,
  • estimator selection,
  • locally efficient,
  • maximum likelihood estimation,
  • model selection,
  • super efficiency,
  • super learning,
  • targeted maximum likelihood estimation,
  • targeted nuisance parameter estimator selection,
  • variable importance
Publication Date
2010
Citation Information
Susan Gruber and Mark J. van der Laan. "An Application of Collaborative Targeted Maximum Likelihood Estimation in Causal Inference and Genomics" The International Journal of Biostatistics Vol. 6 Iss. 1 (2010)
Available at: http://works.bepress.com/mark_van_der_laan/249/