eltmle: Ensemble Learning Targeted Maximum Likelihood Estimation
Harvard | Vancouver
eltmle is a Stata program implementing the targeted maximum likelihood estimation for the ATE for a binary outcome and binary treatment. Future implementations will offer more general settings. eltmle includes the use of a "Super Learner" called from the SuperLearner package v.2.0-21 (Polley E., et al. 2011). The Super-Learner uses V-fold cross-validation (10-fold by default) to assess the performance of prediction regarding the potential outcomes and the propensity score as weighted averages of a set of machine learning algorithms. We used the default SuperLearner algorithms implemented in the base installation of the tmle-R package v.1.2.0-5 (Susan G. and Van der Laan M., 2017), which included the following: i) stepwise selection, ii) generalized linear modeling (glm), iii) a glm variant that included second order polynomials and two-by-two interactions of the main terms included in the model.
|Published in a 3rd party system Date:||9 March 2017|
|Data capture method:||
|Data Creators(s):||Luque-Fernandez, MA|
|LSHTM Faculty/Department:||Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology|
|Participating Institutions:||London School of Hygiene & Tropical Medicine|