{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T21:58:34Z","timestamp":1790632714638,"version":"4.1.0"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2016,1,12]],"date-time":"2016-01-12T00:00:00Z","timestamp":1452556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Johns Hopkins University Bloomberg School of Public Health","award":["U01-AI35042"],"award-info":[{"award-number":["U01-AI35042"]}]},{"name":"Johns Hopkins University Bloomberg School of Public Health","award":["UM1-AI35043"],"award-info":[{"award-number":["UM1-AI35043"]}]},{"DOI":"10.13039\/100007059","name":"Northwestern University","doi-asserted-by":"publisher","award":["U01-AI35039"],"award-info":[{"award-number":["U01-AI35039"]}],"id":[{"id":"10.13039\/100007059","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007185","name":"University of California, Los Angeles","doi-asserted-by":"publisher","award":["U01-AI35040"],"award-info":[{"award-number":["U01-AI35040"]}],"id":[{"id":"10.13039\/100007185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007921","name":"University of Pittsburgh","doi-asserted-by":"publisher","award":["U01-AI35041"],"award-info":[{"award-number":["U01-AI35041"]}],"id":[{"id":"10.13039\/100007921","id-type":"DOI","asserted-by":"publisher"}]},{"name":"JHU CTSA","award":["UL1-TR000424"],"award-info":[{"award-number":["UL1-TR000424"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,9,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Dynamic treatment regimens (DTRs) recommend treatments based on evolving subject-level data. The optimal DTR is that which maximizes expected patient outcome and as such its identification is of primary interest in the personalized medicine setting. When analyzing data from observational studies using semi-parametric approaches, there are two primary components which can be modeled: the expected level of treatment and the expected outcome for a patient given their other covariates. In an effort to offer greater flexibility, the so-called doubly robust methods have been developed which offer consistent parameter estimators as long as at least one of these two models is correctly specified. However, in practice it can be difficult to be confident if this is the case. Using G-estimation as our example method, we demonstrate how the property of double robustness itself can be used to provide evidence that a specified model is or is not correct. 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