Targeted maximum likelihood estimation for a binary treatment: A tutorial

Fernandez, MALORCID logo, Schomaker, M, Rachet, BORCID logo and Schnitzer, ME (2018). Targeted maximum likelihood estimation for a binary treatment: A tutorial. [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.2560802
Copy

When estimating the average effect of a binary treatment (or exposure), methods that incorporate propensity scores, the G-formula, or targeted maximum likelihood estimation (TMLE) are preferred over naive regression approaches which are biased under misspecification of a parametric outcome model. Contrastingly, propensity score methods require the correct specification of an exposure model. Double-robust methods only require correct specification of one of these models. TMLE is a semi-parametric double-robust method that improves the chances of correct model specification by allowing for flexible estimation using non-parametric 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 (i.e., when a study participant had zero 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 and at the following GitHub repository: https://github.com/migariane/SIM-TMLE-tutorial

Keywords

Causal Inference; Machine Learning; Observational Studies; Targeted Maximum Likelihood Estimation; Super Learner; Epidemiology; Statistics

No files available. Please consult associated links.


Atom BibTeX OpenURL ContextObject in Span Multiline CSV OpenURL ContextObject Dublin Core (with Type as Type) MPEG-21 DIDL Data Cite XML EndNote HTML Citation JSON METS MODS RDF+N3 RDF+N-Triples RDF+XML Reference Manager Refer Simple Metadata ASCII Citation EP3 XML
Export