title: An application of collaborative targeted maximum likelihood estimation in causal inference and genomics.
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.
authors: University of California, Berkeley, CA, USA.
authors: Susan
authors: S
authors: Gruber
authors: Mark J
authors: MJ
authors: van der Laan
mesh_terms: D015984:Causality; D024882:Drug Resistance, Viral / Q000235:genetics*; D005260:Female; D023281:Genomics / Q000706:statistics & numerical data; D015658:HIV Infections / Q000188:drug therapy* / Q000235:genetics; D006801:Humans; D016013:Likelihood Functions; D061466:Lopinavir / Q000008:administration & dosage; D008297:Male; D015233:Models, Statistical; D011336:Probability; D057216:Propensity Score
chemical_list: D061466:Lopinavir