@misc{10481/90581,
year = {2023},
url = {https://hdl.handle.net/10481/90581},
abstract = {Purpose: The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates
machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and
robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe
and evaluate the epidemiological applications that have benefited from recent methodological developments.
Methods: We conducted a systematic literature review in PubMed for articles that applied any form of TMLE
in observational studies. We summarized the epidemiological discipline, geographical location, expertize of
the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference
to extract key methodological aspects of the publications. We showcase the contributions to the literature
of these TMLE results.
Results: Of the 89 publications included, 33% originated from the University of California at Berkeley, where
the framework was first developed by Professor Mark van der Laan. By 2022, 59% of the publications originated
from outside the United States and explored up to seven different epidemiological disciplines in
2021–2022. Double-robustness, bias reduction, and model misspecification were the main motivations that
drew researchers toward the TMLE framework. Through time, a wide variety of methodological, tutorial,
and software-specific articles were cited, owing to the constant growth of methodological developments
around TMLE.
Conclusions: There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines
and to increasing numbers of geographical areas. The availability of R packages, publication of
tutorial papers, and involvement of methodological experts in applied publications have contributed to an
exponential increase in the number of studies that understood the benefits and adoption of TMLE.},
organization = {Medical Research Council [grant number MR/W021021/1]},
organization = {Cancer Research UK Population Research Committee Program Award (C7923/A29018)},
publisher = {Elsevier},
keywords = {Targeted Maximum Likelihood Estimation (TMLE)},
keywords = {Epidemiology},
keywords = {Observational studies},
title = {Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review},
doi = {10.1016/j.annepidem.2023.06.004},
author = {Smith, Matthew J. and Phillips, Rachael V. and Luque Fernández, Miguel Ángel and Maringe, Camille},
}