{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T20:53:50Z","timestamp":1770497630995,"version":"3.49.0"},"reference-count":46,"publisher":"Wiley","issue":"23","license":[{"start":{"date-parts":[[2018,5,29]],"date-time":"2018-05-29T00:00:00Z","timestamp":1527552000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"National Eye Institute from National Institute of Health","award":["EY024226"],"award-info":[{"award-number":["EY024226"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2018,10,15]]},"abstract":"<jats:p>This research is motivated by studying the progression of age\u2010related macular degeneration where both a covariate and the response variable are subject to censoring. We develop a general framework to handle regression with censored covariate where the response can be different types and the censoring can be random or subject to (constant) detection limits. Multiple imputation is a popular technique to handle missing data that requires compatibility between the imputation model and the substantive model to obtain valid estimates. With censored covariate, we propose a novel multiple imputation\u2010based approach, namely, the semiparametric two\u2010step importance sampling imputation (STISI) method, to impute the censored covariate. Specifically, STISI imputes the missing covariate from a semiparametric accelerated failure time model conditional on fully observed covariates (Step 1) with the acceptance probability derived from the substantive model (Step 2). The 2\u2010step procedure automatically ensures compatibility and takes full advantage of the relaxed semiparametric assumption in the imputation. Extensive simulations demonstrate that the STISI method yields valid estimates in all scenarios and outperforms some existing methods that are commonly used in practice. We apply STISI on data from the Age\u2010related Eye Disease Study, to investigate the association between the progression time of the less severe eye and that of the more severe eye. We also illustrate the method by analyzing the urine arsenic data for patients from National Health and Nutrition Examination Survey (2003\u20102004) where the response is binary and 1 covariate is subject to detection limit.<\/jats:p>","DOI":"10.1002\/sim.7816","type":"journal-article","created":{"date-parts":[[2018,5,30]],"date-time":"2018-05-30T02:50:26Z","timestamp":1527648626000},"page":"3293-3308","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A semiparametric imputation approach for regression with censored covariate with application to an AMD progression study"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1352-1000","authenticated-orcid":false,"given":"Ying","family":"Ding","sequence":"first","affiliation":[{"name":"Department of Biostatistics University of Pittsburgh  Pittsburgh PA USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengchun","family":"Kong","sequence":"additional","affiliation":[{"name":"Biometrics Department Gilead Science Inc.  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