{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T18:26:23Z","timestamp":1696357583032},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2013,7,3]],"date-time":"2013-07-03T00:00:00Z","timestamp":1372809600000},"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":[[2014,1,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Multiphased designs and biased sampling designs are two of the well-recognized approaches to enhance study efficiency. We propose a new and cost-effective sampling design, the two-phase probability-dependent sampling design, for studies with a continuous outcome. This design will enable investigators to make efficient use of resources by targeting more informative subjects for sampling. We develop a new semiparametric empirical likelihood inference method to take advantage of data obtained through a probability-dependent sampling design. Simulation study results indicate that the sampling scheme proposed, coupled with the proposed estimator, is more efficient and more powerful than the existing outcome-dependent sampling design and the simple random sampling design with the same sample size. We illustrate the method proposed with a real data set from an environmental epidemiologic study.<\/jats:p>","DOI":"10.1111\/rssb.12029","type":"journal-article","created":{"date-parts":[[2013,7,3]],"date-time":"2013-07-03T12:30:39Z","timestamp":1372854639000},"page":"197-215","source":"Crossref","is-referenced-by-count":8,"title":["Semiparametric Inference for Data with A Continuous Outcome from A Two-Phase Probability-Dependent Sampling Scheme"],"prefix":"10.1093","volume":"76","author":[{"given":"Haibo","family":"Zhou","sequence":"first","affiliation":[{"name":"University of North Carolina at Chapel Hill USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wangli","family":"Xu","sequence":"additional","affiliation":[{"name":"Renmin University of China , Beijing , People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donglin","family":"Zeng","sequence":"additional","affiliation":[{"name":"University of North Carolina at Chapel Hill USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwen","family":"Cai","sequence":"additional","affiliation":[{"name":"University of North Carolina at Chapel Hill USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2013,7,3]]},"reference":[{"key":"2023031306055210400_","volume-title":"Advanced Econometrics","author":"Amemiya","year":"1985"},{"key":"2023031306055210400_","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1093\/biomet\/59.1.19","article-title":"Separate sample logistic discrimination","volume":"59","author":"Anderson","year":"1972","journal-title":"Biometrika"},{"key":"2023031306055210400_","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1093\/biomet\/75.1.11","article-title":"Logistic regression for two-stage case-control data","volume":"75","author":"Breslow","year":"1988","journal-title":"Biometrika"},{"key":"2023031306055210400_","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1111\/1467-9868.00078","article-title":"Maximum likelihood estimation of logistic regression parameters under two-phase, outcome-dependent sampling","volume":"59","author":"Breslow","year":"1997","journal-title":"J. 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