An Application of Collaborative Targeted Maximum Likelihood Estimation in Causal Inference and Genomics

作者：Gruber Susan^*; van der Laan Mark J

来源：International Journal of Biostatistics, 2010, 6(1): 18.

DOI：10.2202/1557-4679.1182

• 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

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 antiretroviral 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.

• 出版日期2010