Vendredi le 25 mars 2011, 15:35, McGill University, Burnside Hall, 805, rue Sherbrooke O., salle 1B24

Targeted Maximum Likelihood Estimation: Assessing Effects in RCT and Observational Studies
Mark van der Laan, Jiann-Ping Hsu/Karl E. Peace, UC Berkeley

In this talk we present targeted maximum likelihood based estimators of a causal effect defined in realistic semiparametric models for the data generating experiment, that takes away the need for specifying regression models. Fundamental concepts underlying this methodology are careful definition of the target parameter of the data generating distribution in a realistic semiparametric model, super Learning, i.e., the very aggressive use of cross-validation to select optimal combinations of many candidate estimators, and subsequent targeted maximum likelihood estimation to target the fit towards the causal effect/target parameter of interest. We demonstrate the performance in simulation studies. We also illustrate this method for assessing causal effects of treatment on clinical outcomes in RCT and observational studies in HIV. In particular, we demonstrate the TMLE that allows right-censoring to be a function of time-dependent covariates.