{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T05:40:39Z","timestamp":1682919639113},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2003,10,28]],"date-time":"2003-10-28T00:00:00Z","timestamp":1067299200000},"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":[],"published-print":{"date-parts":[[2003,11,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Consider a clinical trial in which participants are randomized to a single-dose treatment or a placebo control and assume that the adherence level is accurately recorded. If the treatment is effective, then good adherers in the treatment group should do better than poor adherers because they received more drug; the treatment group data follow a dose\u2013response curve. But, good adherers to the placebo often do better than poor adherers, so the observed adherence\u2013response in the treatment group cannot be completely attributed to the treatment. Efron and Feldman proposed an adjustment to the observed adherence\u2013response in the treatment group by using the adherence\u2013response in the control group. It relies on a percentile invariance assumption under which each participant\u2019s adherence percentile within their assigned treatment group does not depend on the assigned group (active drug or placebo). The Efron and Feldman approach is valid under percentile invariance, but not necessarily under departures from it. We propose an analysis based on a generalization of percentile invariance that allows adherence percentiles to be stochastically permuted across treatment groups, using a broad class of stochastic permutation models. We show that approximate maximum likelihood estimates of the underlying dose\u2013response curve perform well when the stochastic permutation process is correctly specified and are quite robust to model misspecification.<\/jats:p>","DOI":"10.1111\/1467-9868.00418","type":"journal-article","created":{"date-parts":[[2004,12,28]],"date-time":"2004-12-28T19:39:11Z","timestamp":1104262751000},"page":"837-849","source":"Crossref","is-referenced-by-count":1,"title":["Accommodating Stochastic Departures from Percentile Invariance in Causal Models"],"prefix":"10.1093","volume":"65","author":[{"given":"Kevin K.","family":"Dobbin","sequence":"first","affiliation":[{"name":"National Cancer Institute , Bethesda , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas A.","family":"Louis","sequence":"additional","affiliation":[{"name":"Johns Hopkins Bloomberg School of Public Health , Baltimore , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2003,10,28]]},"reference":[{"key":"2023032921501558900_","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1080\/01621459.1996.10476902","article-title":"Identification of causal effects using instrumental variables","volume":"91","author":"Angrist","year":"1996","journal-title":"J. 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