  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. We conducted a systematic literature review
in PubMed for articles that applied any form of TMLE in observational studies.
We summarised the epidemiological discipline, geographical location, expertise
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. Of the 81 publications included, 25% originated from the University of
California at Berkeley, where the framework was first developed by Professor
Mark van der Laan. By the first half of 2022, 70% of the publications
originated from outside the United States and explored up to 7 different
epidemiological disciplines in 2021-22. Double-robustness, bias reduction and
model misspecification were the main motivations that drew researchers towards
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. 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.
