Luque-Fernandez, Miguel AngelSchomaker, MichaelRachet, BernardSchnitzer, Mireille E2023-01-252023-01-252018-01-09Luque-Fernandez MA, Schomaker M, Rachet B, Schnitzer ME. Targeted maximum likelihood estimation for a binary treatment: A tutorial. Stat Med. 2018 Jul 20;37(16):2530-2546http://hdl.handle.net/10668/12381When estimating the average effect of a binary treatment (or exposure) on an outcome, methods that incorporate propensity scores, the G-formula, or targeted maximum likelihood estimation (TMLE) are preferred over naïve regression approaches, which are biased under misspecification of a parametric outcome model. In contrast propensity score methods require the correct specification of an exposure model. Double-robust methods only require correct specification of either the outcome or the exposure model. Targeted maximum likelihood estimation is a semiparametric double-robust method that improves the chances of correct model specification by allowing for flexible estimation using (nonparametric) machine-learning methods. It therefore requires weaker assumptions than its competitors. We provide a step-by-step guided implementation of TMLE and illustrate it in a realistic scenario based on cancer epidemiology where assumptions about correct model specification and positivity (ie, when a study participant had 0 probability of receiving the treatment) are nearly violated. This article provides a concise and reproducible educational introduction to TMLE for a binary outcome and exposure. The reader should gain sufficient understanding of TMLE from this introductory tutorial to be able to apply the method in practice. Extensive R-code is provided in easy-to-read boxes throughout the article for replicability. Stata users will find a testing implementation of TMLE and additional material in the Appendix S1 and at the following GitHub repository: https://github.com/migariane/SIM-TMLE-tutorial.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/causal inferenceensemble Learningmachine learningobservational studiestargeted maximum likelihood estimationAlgorithmsComputer SimulationData Interpretation, StatisticalEpidemiologic MethodsHumansLikelihood FunctionsMachine LearningNeoplasmsPropensity Scoreresearch article29687470open accessAlgoritmosAprendizaje automáticoFunciones de verosimilitudHumanosInterpretación estadística de datosMétodos epidemiológicosNeoplasiasPuntaje de propensiónSimulación por computador10.1002/sim.76281097-0258PMC6032875https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/sim.7628https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6032875/pdf