{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T20:57:23Z","timestamp":1771102643279,"version":"3.50.1"},"reference-count":28,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2019,11,23]],"date-time":"2019-11-23T00:00:00Z","timestamp":1574467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Path-specific effects constitute a broad class of mediated effects from an exposure to an outcome via one or more causal pathways along a set of intermediate variables. Most of the literature concerning estimation of mediated effects has focused on parametric models, with stringent assumptions regarding unmeasured confounding. We consider semiparametric inference of a path-specific effect when these assumptions are relaxed. In particular, we develop a suite of semiparametric estimators for the effect along a pathway through a mediator, but not through an exposure-induced confounder of that mediator. These estimators have different robustness properties, as each depends on different parts of the likelihood of the observed data. One estimator is locally semiparametric efficient and multiply robust. The latter property implies that machine learning can be used to estimate nuisance functions. We demonstrate these properties, as well as finite-sample properties of all the estimators, in a simulation study. We apply our method to an HIV study, in which we estimate the effect comparing two drug treatments on a patient\u2019s average log CD4 count mediated by the patient\u2019s level of adherence, but not by previous experience of toxicity, which is clearly affected by which treatment the patient is assigned to and may confound the effect of the patient\u2019s level of adherence on their virologic outcome.<\/jats:p>","DOI":"10.1093\/biomet\/asz063","type":"journal-article","created":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T19:47:32Z","timestamp":1570736852000},"source":"Crossref","is-referenced-by-count":6,"title":["On semiparametric estimation of a path-specific effect in the presence of mediator-outcome confounding"],"prefix":"10.1093","author":[{"given":"C H","family":"Miles","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Columbia University Mailman School of Public Health, 722 West 168th Street, New York, New York 10032, U.S.A"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"I","family":"Shpitser","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Johns Hopkins University, 160 Malone Hall, 3400 N. Charles Street, Baltimore, Maryland 21218, U.S.A"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P","family":"Kanki","sequence":"additional","affiliation":[{"name":"Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, 665 Huntington Avenue, Boston, Massachusetts 02115, U.S.A"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S","family":"Meloni","sequence":"additional","affiliation":[{"name":"Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, 665 Huntington Avenue, Boston, Massachusetts 02115, U.S.A"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E J","family":"Tchetgen Tchetgen","sequence":"additional","affiliation":[{"name":"Department of Statistics, The Wharton School, University of Pennsylvania, 467 Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, Pennsylvania 19104, U.S.A"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,11,23]]},"reference":[{"key":"2019112308104005800_B1","first-page":"357","volume-title":"Proc. 19th Int. Joint Conf. Artif. Intel. 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Meth."},{"key":"2019112308104005800_B6","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1080\/01621459.2017.1422737","article-title":"Nonparametric causal effects based on incremental propensity score interventions","volume":"114","author":"Kennedy,","year":"2019","journal-title":"J. Am. Statist. Assoc."},{"key":"2019112308104005800_B7","article-title":"Sequential double robustness in right-censored longitudinal models","volume-title":"arXiv:","author":"Luedtke,","year":"2018"},{"key":"2019112308104005800_B8","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1080\/01621459.2017.1295862","article-title":"Quantifying an adherence path-specific effect of antiretroviral therapy in the Nigeria PEPFAR program","volume":"112","author":"Miles,","year":"2017","journal-title":"J. Am. Statist. 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Causal Infer."}],"container-title":["Biometrika"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/biomet\/advance-article-pdf\/doi\/10.1093\/biomet\/asz063\/31102098\/asz063.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/biomet\/advance-article-pdf\/doi\/10.1093\/biomet\/asz063\/31102098\/asz063.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,23]],"date-time":"2019-11-23T13:11:15Z","timestamp":1574514675000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biomet\/advance-article\/doi\/10.1093\/biomet\/asz063\/5638939"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,23]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1093\/biomet\/asz063","relation":{},"ISSN":["0006-3444","1464-3510"],"issn-type":[{"value":"0006-3444","type":"print"},{"value":"1464-3510","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,23]]},"article-number":"asz063"}}