Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review

• URL: http://arxiv.org/abs/2303.07329v1
• Date: Mon, 13 Mar 2023 17:50:03 GMT
• Title: Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review
• Authors: Matthew J. Smith, Rachael V. Phillips, Miguel Angel Luque-Fernandez, Camille Maringe
• Abstract summary: The Targeted Maximum Likelihood Estimation framework integrates machine learning, statistical theory, and statistical inference. We conduct a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. Of the 81 publications included, 25% originated from the University of California at Berkeley, where the framework was first developed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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