{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:08:41Z","timestamp":1767650921313},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2019,10,15]],"date-time":"2019-10-15T00:00:00Z","timestamp":1571097600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01\u00a0AI085073"],"award-info":[{"award-number":["R01\u00a0AI085073"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biometrics"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Interference occurs between individuals when the treatment (or exposure) of one individual affects the outcome of another individual. Previous work on causal inference methods in the presence of interference has focused on the setting where it is a priori assumed that there is \u201cpartial interference,\u201d in the sense that individuals can be partitioned into groups wherein there is no interference between individuals in different groups. Bowers <jats:italic>et al.<\/jats:italic> (2012, <jats:italic>Political Anal<\/jats:italic>, 21, 97\u2013124) and Bowers <jats:italic>et al.<\/jats:italic> (2016, <jats:italic>Political Anal<\/jats:italic>, 24, 395\u2013403) consider randomization\u2010based inferential methods that allow for more general interference structures in the context of randomized experiments. In this paper, extensions of Bowers <jats:italic>et al<\/jats:italic>. that allow for failure time outcomes subject to right censoring are proposed. Permitting right\u2010censored outcomes is challenging because standard randomization\u2010based tests of the null hypothesis of no treatment effect assume that whether an individual is censored does not depend on treatment. The proposed extension of Bowers <jats:italic>et al<\/jats:italic>. to allow for censoring entails adapting the method of Wang <jats:italic>et al.<\/jats:italic> (2010, <jats:italic>Biostatistics<\/jats:italic>, 11, 676\u2013692) for two\u2010sample survival comparisons in the presence of unequal censoring. The methods are examined via simulation studies and utilized to assess the effects of cholera vaccination in an individually randomized trial of 73\u2009000 children and women in Matlab, Bangladesh.<\/jats:p>","DOI":"10.1111\/biom.13125","type":"journal-article","created":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T07:54:21Z","timestamp":1565164461000},"page":"235-245","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Randomization inference with general interference and censoring"],"prefix":"10.1093","volume":"76","author":[{"given":"Wen Wei","family":"Loh","sequence":"first","affiliation":[{"name":"Department of Data Analysis Ghent University Gent Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael G.","family":"Hudgens","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of North Carolina Chapel Hill North Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John D.","family":"Clemens","sequence":"additional","affiliation":[{"name":"Department of Epidemiology University of California Los Angeles California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of International Health Johns Hopkins University Baltimore Maryland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael E.","family":"Emch","sequence":"additional","affiliation":[{"name":"Department of Geography University of North Carolina Chapel Hill North Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,10,15]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(05)66550-6"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2016.1241178"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/pan\/mpw018"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/pan\/mps038"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/infdis\/158.1.60"},{"key":"e_1_2_8_7_1","volume-title":"Modelling Survival Data in Medical Research","author":"Collett D.","year":"2003"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.socscimed.2008.11.025"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40471-016-0086-4"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214508000000292"},{"key":"e_1_2_8_11_1","unstructured":"Jagadeesan R. Pillai N.andVolfovsky A.(2017)Designs for estimating the treatment effect in networks with interference. Available at:https:\/\/arxiv.org\/abs\/1705.08524"},{"key":"e_1_2_8_12_1","volume-title":"Testing Statistical Hypotheses. Springer Texts in Statistics","author":"Lehmann E.L.","year":"2005"},{"key":"e_1_2_8_13_1","unstructured":"Ogburn E.L. Sofrygin O. Diaz I.andvan derLaan M.J.(2017)Causal inference for social network data. Available at:https:\/\/arxiv.org\/abs\/1705.08527"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12184"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0022971"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3692-2"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000001112"},{"key":"e_1_2_8_18_1","unstructured":"S\u00e4vje F. Aronow P.M.andHudgens M.G.(2017)Average treatment effects in the presence of unknown interference. Available at:https:\/\/arxiv.org\/abs\/1711.06399"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000000636"},{"key":"e_1_2_8_20_1","unstructured":"Sussman D.L.andAiroldi E.M.(2017)Elements of estimation theory for causal effects in the presence of network interference. 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