{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T12:00:44Z","timestamp":1784808044081,"version":"3.55.0"},"reference-count":18,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Eur J Clin Investigation"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The g formula is a cornerstone method for estimating causal effects of time\u2010varying treatments using longitudinal observational data in the presence of time\u2010varying confounders that are affected by prior treatment. Standard regression techniques often fail in this setting because adjustment for such covariates can distort the very effects under study. The g\u2010formula addresses this problem by expressing the mean potential outcome under specified static or dynamic treatment regimes as a function of the joint distribution of covariates, treatments and outcomes, which can be approximated via parametric or semi\u2010parametric models and simulation. This review presents the g\u2010formula, emphasizing intuitive explanations. After outlining the causal framework and the core identification assumptions\u2014consistency, sequential exchangeability and positivity\u2014the article describes practical parametric g\u2010formula: model specification for covariate and outcome processes, implementation via forward simulation, and the interpretation of marginal causal contrasts between clinically relevant regimes. The g\u2010formula is then situated within the family of g\u2010methods alongside inverse probability weighting and targeted maximum likelihood estimation, highlighting complementary strengths and limitations. A dedicated section discusses concrete applications, including analyses of highly active antiretroviral therapy and AIDS or death, dynamic \u2018when to start\u2019 antiretroviral strategies in HIV and electronic health record\u2010based evaluations of blood pressure treatment targets. Practical guidance on modelling choices, diagnostics and transparent reporting is provided to support applied researchers considering g\u2010formula in clinical and epidemiological investigations.<\/jats:p>","DOI":"10.1111\/eci.70239","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:36:46Z","timestamp":1782848206000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Causal Effects of Time\u2010Varying Treatments: The G\u2010Formula"],"prefix":"10.1111","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6616-1996","authenticated-orcid":false,"given":"Carmine","family":"Zoccali","sequence":"first","affiliation":[{"name":"Renal Research Institute  New York New York USA"},{"name":"Institute of Molecular Biology and Genetics (Biogem)  Ariano Irpino Italy"},{"name":"Associazione Ipertensione Nefrologia Trapianto Renale (IPNET)  Reggio Calabria Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Tripepi","sequence":"additional","affiliation":[{"name":"Research Unit of Clinical Epidemiology CNR\u2010IFC, Institute of Clinical Physiology  Pisa Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"e_1_2_17_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/0270-0255(86)90088-6"},{"key":"e_1_2_17_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0021-9681(87)80018-8"},{"key":"e_1_2_17_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.5316"},{"key":"e_1_2_17_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/maq.12618"},{"key":"e_1_2_17_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0000000000000043"},{"key":"e_1_2_17_7_1","first-page":"180","article-title":"Causal Inference on Electronic Health Records to Assess Blood Pressure Treatment Targets: An Application of the Parametric g Formula","volume":"23","author":"Johnson K. W.","year":"2018","journal-title":"Pacific Symposium on Biocomputing"},{"key":"e_1_2_17_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/1475-6773.13212"},{"key":"e_1_2_17_9_1","first-page":"1362","article-title":"An Introduction to g Methods","volume":"47","author":"Ahern J.","year":"2018","journal-title":"International Journal of Epidemiology"},{"key":"e_1_2_17_10_1","doi-asserted-by":"publisher","DOI":"10.1097\/00001648-200009000-00011"},{"key":"e_1_2_17_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12561-011-9040-7"},{"key":"e_1_2_17_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-81110-0"},{"key":"e_1_2_17_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10985-025-09652-3"},{"key":"e_1_2_17_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13321"},{"key":"e_1_2_17_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/S12874\u2010023\u201001835\u20106"},{"key":"e_1_2_17_16_1","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0000000000001233"},{"key":"e_1_2_17_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2020.100008"},{"key":"e_1_2_17_18_1","volume-title":"GFORMULA: SAS Macro Implementing the Parametric g\u2010Formula","author":"Causal Inference Group (CAUSALab)","year":"2026"},{"key":"e_1_2_17_19_1","doi-asserted-by":"publisher","DOI":"10.1177\/1536867X1201100401"}],"container-title":["European Journal of Clinical Investigation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/eci.70239","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/eci.70239","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/eci.70239","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T11:02:38Z","timestamp":1784804558000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/eci.70239"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":18,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["10.1111\/eci.70239"],"URL":"https:\/\/doi.org\/10.1111\/eci.70239","archive":["Portico"],"relation":{},"ISSN":["0014-2972","1365-2362"],"issn-type":[{"value":"0014-2972","type":"print"},{"value":"1365-2362","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"2026-04-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70239"}}