{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T21:27:16Z","timestamp":1771363636675,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T00:00:00Z","timestamp":1734134400000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Discovery Launch Supplement","award":["20R12709"],"award-info":[{"award-number":["20R12709"]}]},{"name":"M.E.K.\u2019s Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery","award":["20R01603"],"award-info":[{"award-number":["20R01603"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Within epidemiologic research, estimating treatment effects from observational data presents notable challenges. Targeted maximum likelihood estimation (TMLE) emerges as a robust method, addressing these challenges by accurately modeling treatment effects. This approach uniquely combines the precision of correctly specified models with the versatility of data-adaptive, flexible machine learning algorithms. Despite its effectiveness, TMLE\u2019s integration of complex algorithms can introduce bias and undercoverage. This issue is addressed through the double cross-fit TMLE (DC-TMLE) approach, enhancing accuracy and reducing biases inherent in observational studies. However, DC-TMLE\u2019s potential remains underexplored in epidemiologic research, primarily due to the lack of comprehensive methodologic guidance and the complexity of its computational implementation. Recognizing this gap, our article contributes a detailed, reproducible guide for implementing DC-TMLE in R, aimed specifically at epidemiologic applications. We demonstrate the utility of this method using an openly available clinical data set, underscoring its relevance and adaptability for robust epidemiologic analysis. This guide aims to facilitate broader adoption of DC-TMLE in epidemiologic studies, promoting more accurate and reliable treatment effect estimations in observational research.<\/jats:p>","DOI":"10.1093\/aje\/kwae447","type":"journal-article","created":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T16:39:53Z","timestamp":1734194393000},"page":"2813-2819","source":"Crossref","is-referenced-by-count":5,"title":["Towards robust causal inference in epidemiologic research: employing double cross-fit TMLE in right heart catheterization data"],"prefix":"10.1093","volume":"194","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2624-2118","authenticated-orcid":false,"given":"Momenul Haque","family":"Mondol","sequence":"first","affiliation":[{"name":"School of Population and Public Health, University of British Columbia , Vancouver, BC V6T 1Z3 ,","place":["Canada"]},{"name":"Department of Statistics, University of Barishal , Barishal-8254 ,","place":["Bangladesh"]},{"name":"Centre for Advancing Health Outcomes, University of British Columbia , Vancouver, BC V6Z 1Y6 ,","place":["Canada"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0346-2871","authenticated-orcid":false,"given":"Mohammad Ehsanul","family":"Karim","sequence":"additional","affiliation":[{"name":"School of Population and Public Health, University of British Columbia , Vancouver, BC V6T 1Z3 ,","place":["Canada"]},{"name":"Centre for Advancing Health Outcomes, University of British Columbia , Vancouver, BC V6Z 1Y6 ,","place":["Canada"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"issue":"9","key":"2025101606063702600_ref1","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1093\/aje\/kwp015","article-title":"Estimating the effects of potential public health interventions on population disease burden: a step-by-step illustration of causal inference 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