ArticleStatistics in medicine2018
Targeted maximum likelihood estimation for a binary treatment: A tutorial.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
72 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
- Use of sensitivity analyses to assess uncontrolled confounding from unmeasured variables in observational, active comparator pharmacoepidemiologic studies: a systematic review.American journal of epidemiology · 2025Pooled it
- Association Between Telomere Length and Skin Cancer and Aging: A Mendelian Randomization Analysis.Frontiers in genetics · 2022Pooled it
- Far from MCAR: Obtaining Population-level Estimates of HIV Viral Suppression.Epidemiology (Cambridge, Mass.) · 2020Trial
- The association between food insecurity and poor sleep quality among transgender women of color: The TURNNT cohort study.The journal of LGBTQ+ mental health · 2026Article
- Impact of the Social Media Minimum Age on Australian young people and parents: protocol for a mixed-methods evaluation.BMJ open · 2026Article
- Cost-effectiveness of the IMPALA monitoring system for hospitalised children in low-resource settings: a pragmatic before-and-after study.BMJ paediatrics open · 2026Article
- Blood pressure thresholds for the administration of balanced crystalloids and the effect on intra-operative hypotension: A proof-of-concept causal inference analysis featuring idealised dynamic treatment regimens.European journal of anaesthesiology · 2026Observational
- Causal Effect Estimation With TMLE: Handling Missing Data and Near Violations of Positivity.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Use of causal inference methods in case-control studies: a methodology review.American journal of epidemiology · 2026Review
- A review and evaluation of doubly robust approaches for estimating average treatment effects.Behavior research methods · 2026Review
- Participation in the house-ball community as a social determinant of HIV care and prevention outcomes among transgender women of color.Social science & medicine (1982) · 2026Article
- Identifying and avoiding design related biases in observational studies using the target trial framework.BMJ medicine · 2026Article
- Universal same-day discharge after colonic resection: a matched cohort study.Updates in surgery · 2025Article
- Anxiety symptoms during pregnancy and risk of adverse birth outcomes in Gondar Town Ethiopia.Scientific reports · 2025Article
- Towards robust causal inference in epidemiologic research: employing double cross-fit TMLE in right heart catheterization data.American journal of epidemiology · 2025Article
- The effect of lifting eviction moratoria on fatal drug overdoses in the context of the COVID-19 pandemic in the United States.American journal of epidemiology · 2025Article
- Estimating the causal effects of exposure mixtures: a generalized propensity score method.BMC medical research methodology · 2025Article
- Causal Machine Learning Methods and Use of Cross-Fitting in Settings With High-Dimensional Confounding.Statistics in medicine · 2025Article
- Article
12 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
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.
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