English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Targeted Maximum Likelihood Estimation (TMLE) statistical data analysisframework integrates machine learning, statistical theory, and statisticalinference to provide a least biased, efficient and robust strategy forestimation and inference of a variety of statistical and causal parameters. Wedescribe and evaluate the epidemiological applications that have benefited fromrecent methodological developments. We conducted a systematic literature reviewin PubMed for articles that applied any form of TMLE in observational studies.We summarised the epidemiological discipline, geographical location, expertiseof the authors, and TMLE methods over time. We used the Roadmap of TargetedLearning and Causal Inference to extract key methodological aspects of thepublications. We showcase the contributions to the literature of these TMLEresults. Of the 81 publications included, 25% originated from the University ofCalifornia at Berkeley, where the framework was first developed by ProfessorMark van der Laan. By the first half of 2022, 70% of the publicationsoriginated from outside the United States and explored up to 7 differentepidemiological disciplines in 2021-22. Double-robustness, bias reduction andmodel misspecification were the main motivations that drew researchers towardsthe TMLE framework. Through time, a wide variety of methodological, tutorialand software-specific articles were cited, owing to the constant growth ofmethodological developments around TMLE. There is a clear dissemination trendof the TMLE framework to various epidemiological disciplines and to increasingnumbers of geographical areas. The availability of R packages, publication oftutorial papers, and involvement of methodological experts in appliedpublications have contributed to an exponential increase in the number ofstudies that understood the benefits, and adoption, of TMLE.

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