Targeted maximum likelihood estimation for a binary treatment: A tutorial

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 ou...

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Bibliographic Details
Published in:Statistics in medicine 2018-07, Vol.37 (16), p.2530-2546
Main Authors: Luque‐Fernandez, Miguel Angel, Schomaker, Michael, Rachet, Bernard, Schnitzer, Mireille E.
Format: Article
Language:English
Subjects:
Algorithms
causal inference
Computer Simulation
Data Interpretation, Statistical
Economic models
ensemble Learning
Epidemiologic Methods
Humans
Likelihood Functions
Machine Learning
Neoplasms - epidemiology
Observational studies
Propensity Score
Regression analysis
targeted maximum likelihood estimation
Tutorial in Biostatistics
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Summary: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
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.7628