RT Journal Article
T1 Targeted maximum likelihood estimation for a binary treatment: A tutorial.
A1 Luque-Fernandez, Miguel Angel
A1 Schomaker, Michael
A1 Rachet, Bernard
A1 Schnitzer, Mireille E
K1 causal inference
K1 ensemble Learning
K1 machine learning
K1 observational studies
K1 targeted maximum likelihood estimation
AB When 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.
PB John Wiley & Sons Ltd.
YR 2018
FD 2018-01-09
LK http://hdl.handle.net/10668/12381
UL http://hdl.handle.net/10668/12381
LA en
NO Luque-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):2530-2546
NO This work was supported by Cancer Research UK grant number C7923/A18525. M.A.L.F. is supported by a Miguel Servet I Investigator Award (grant CP17/00206) from the Carlos III Institute of Health, and M.E.S. is supported by a Canadian Institutes of Health Research New Investigator Award. The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the funding agencies. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication
DS RISalud
RD Apr 18, 2025