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Recently we proposed a structural framework for population-based cancer epidemiology and evaluate the performance of doubly robust estimators for a binary exposure in cancer mortality. In simulation studies, we have demonstrated that TMLE shows the best bias-variance trade-off, more precise estimates, and more appropriate 95% confidence interval coverage than its competitors, supporting the use of the data-adaptive model selection strategies based on machine-learning algorithms. We applied TMLE to estimate adjusted one-year mortality risk differences in 183,426 lung cancer patients diagnosed after admission to an emergency department versus non-emergency cancer diagnosis in England, 2006–2013. The adjusted mortality risk (for patients diagnosed with lung cancer after admission to an emergency department) was 16% higher in men and 18% higher in women, suggesting the importance of interventions targeting early detection of lung cancer signs and symptoms.
eltmle is a Stata program implementing the targeted maximum likelihood estimation for the ATE for a binary or continuous 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.
Luque-Fernandez MA, (2017). Ensemble Targeted Maximum Likelihood Estimation for a Binary Outcome and Treatment. GitHub repository, https://github.com/migariane/weltmle (Windows users) or https://github.com/migariane/meltmle (MAC users)
Miguel Angel Luque-Fernandez