Hypothesis testing in outcome-dependent sampling design under generalized linear models
In many large cohort studies, the major budge and cost typically arise from the assembling of primary covariates. Outcome-dependent sampling (ODS) designs are cost-effective sampling schemes which enrich the observed sample by selectively including certain subjects. We study the inference methods of...
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| Published in | Communications in statistics. Simulation and computation Vol. 51; no. 4; pp. 1721 - 1745 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Taylor & Francis
03.04.2022
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| Abstract | In many large cohort studies, the major budge and cost typically arise from the assembling of primary covariates. Outcome-dependent sampling (ODS) designs are cost-effective sampling schemes which enrich the observed sample by selectively including certain subjects. We study the inference methods of hypothesis testing for a general ODS design under the generalized linear models. We develop a profile-likelihood-based family of tests and propose likelihood-ratio, Wald and score test statistics. Asymptotic properties of the proposed tests are established and the null limiting distributions are derived. The finite-sample behavior of the proposed methods is evaluated through simulation studies, and an application to a Wilms tumor data are illustrated. |
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| AbstractList | In many large cohort studies, the major budge and cost typically arise from the assembling of primary covariates. Outcome-dependent sampling (ODS) designs are cost-effective sampling schemes which enrich the observed sample by selectively including certain subjects. We study the inference methods of hypothesis testing for a general ODS design under the generalized linear models. We develop a profile-likelihood-based family of tests and propose likelihood-ratio, Wald and score test statistics. Asymptotic properties of the proposed tests are established and the null limiting distributions are derived. The finite-sample behavior of the proposed methods is evaluated through simulation studies, and an application to a Wilms tumor data are illustrated. |
| Author | Ding, Jieli Zhang, Haodong |
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| Cites_doi | 10.1214/aos/1176346597 10.1198/016214503388619184 10.1200/JCO.1998.16.1.237 10.1002/1097-0142(19890715)64:2<349::AID-CNCR2820640202>3.0.CO;2-Q 10.1111/j.1541-0420.2010.01446.x 10.1111/j.0006-341X.2002.00413.x 10.1093/oxfordjournals.aje.a113266 10.1080/01621459.1993.10476416 10.1093/biomet/86.4.755 10.1093/biostatistics/kxq070 10.2307/2532141 10.1093/biomet/75.1.11 10.1198/016214504000001853 10.1093/biomet/73.1.1 10.1093/biomet/asn073 10.1093/biomet/asp059 10.1093/biostatistics/kxu016 10.1002/cjs.11257 10.1007/978-1-4899-3242-6 10.1198/016214504000000584 10.1111/1467-9876.00165 10.1002/cjs.11131 10.1007/s10985-015-9355-7 10.1007/s11425-016-0152-4 10.1214/aos/1059655907 |
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| SubjectTerms | Biased sampling Likelihood ratio test Score test Semiparametric empirical likelihood Wald test |
| Title | Hypothesis testing in outcome-dependent sampling design under generalized linear models |
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