{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T01:35:59Z","timestamp":1790818559921,"version":"4.1.0"},"reference-count":43,"publisher":"Wiley","issue":"16","license":[{"start":{"date-parts":[[2018,4,23]],"date-time":"2018-04-23T00:00:00Z","timestamp":1524441600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004587","name":"Instituto de Salud Carlos III","doi-asserted-by":"publisher","award":["CP17\/00206"],"award-info":[{"award-number":["CP17\/00206"]}],"id":[{"id":"10.13039\/501100004587","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["C7923\/A18525"],"award-info":[{"award-number":["C7923\/A18525"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2018,7,20]]},"abstract":"<jats:p>\n                    When estimating the average effect of a binary treatment (or exposure) on an outcome, methods that incorporate propensity scores, the G\u2010formula, or targeted maximum likelihood estimation (TMLE) are preferred over na\u00efve regression approaches, which are biased under misspecification of a parametric outcome model. In contrast propensity score methods require the correct specification of an exposure model. Double\u2010robust methods only require correct specification of either the outcome or the exposure model. Targeted maximum likelihood estimation is a semiparametric double\u2010robust method that improves the chances of correct model specification by allowing for flexible estimation using (nonparametric) machine\u2010learning methods. It therefore requires weaker assumptions than its competitors. We provide a step\u2010by\u2010step guided implementation of TMLE and illustrate it in a realistic scenario based on cancer epidemiology where assumptions about correct model specification and positivity (ie, when a study participant had 0 probability of receiving the treatment) are nearly violated. This article provides a concise and reproducible educational introduction to TMLE for a binary outcome and exposure. The reader should gain sufficient understanding of TMLE from this introductory tutorial to be able to apply the method in practice. Extensive R\u2010code is provided in easy\u2010to\u2010read boxes throughout the article for replicability. Stata users will find a testing implementation of TMLE and additional material in the\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"#sim7628-supitem-0002\">Appendix S1<\/jats:ext-link>\n                    and at the following GitHub repository:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/migariane\/SIM-TMLE-tutorial\">https:\/\/github.com\/migariane\/SIM\u2010TMLE\u2010tutorial<\/jats:ext-link>\n                  <\/jats:p>","DOI":"10.1002\/sim.7628","type":"journal-article","created":{"date-parts":[[2018,4,23]],"date-time":"2018-04-23T21:20:56Z","timestamp":1524518456000},"page":"2530-2546","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":133,"title":["Targeted maximum likelihood estimation for a binary treatment: A tutorial"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6683-5164","authenticated-orcid":false,"given":"Miguel Angel","family":"Luque\u2010Fernandez","sequence":"first","affiliation":[{"name":"Cancer Survival Group, Department of Non\u2010Communicable Disease Epidemiology Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine  London UK"},{"name":"Department of Epidemiology Harvard T.H. Chan School of Public Health  Boston MA USA"},{"name":"Biomedical Research Institute of Granada, Non\u2010Communicable and Cancer Epidemiology Group (ibs.Granada) Andalusian School of Public Health  Granada Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8475-0591","authenticated-orcid":false,"given":"Michael","family":"Schomaker","sequence":"additional","affiliation":[{"name":"School of Public Health and Family Medicine, Center for Infectious Disease Epidemiology and Research The University of Cape Town  Cape Town South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernard","family":"Rachet","sequence":"additional","affiliation":[{"name":"Cancer Survival Group, Department of Non\u2010Communicable Disease Epidemiology Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8049-9646","authenticated-orcid":false,"given":"Mireille E.","family":"Schnitzer","sequence":"additional","affiliation":[{"name":"Facult\u00e9 de pharmacie Universit\u00e9 de Montr\u00e9al  Montr\u00e9al Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,4,23]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161"},{"key":"e_1_2_7_3_1","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1097\/00001648-200009000-00011","article-title":"structural models and causal inference in epidemiology","author":"Robins JM","year":"2000","journal-title":"Epidemiology"},{"key":"e_1_2_7_4_1","volume-title":"Modern Epidemiology","author":"Rothman K","year":"2016"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001880"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0037350"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/15.3.413"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0000000000000160"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwq472"},{"key":"e_1_2_7_10_1","volume-title":"Identification for Prediction and Decision. 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