| Double-Robust Semiparametric Estimator for Differences in Restricted Mean Lifetimes in Observational Studies. | |
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MedLine Citation:
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PMID: 22471876 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Restricted mean lifetime is often of direct interest in epidemiologic studies involving censored survival times. Differences in this quantity can be used as a basis for comparing several groups. For example, transplant surgeons, nephrologists, and of course patients are interested in comparing posttransplant lifetimes among various types of kidney transplants to assist in clinical decision making. As the factor of interest is not randomized, covariate adjustment is needed to account for imbalances in confounding factors. In this report, we use semiparametric theory to develop an estimator for differences in restricted mean lifetimes although accounting for confounding factors. The proposed method involves building working models for the time-to-event and coarsening mechanism (i.e., group assignment and censoring). We show that the proposed estimator possesses the double robust property; i.e., when either the time-to-event or coarsening process is modeled correctly, the estimator is consistent and asymptotically normal. Simulation studies are conducted to assess its finite-sample performance and the method is applied to national kidney transplant data. |
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Authors:
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Min Zhang; Douglas E Schaubel |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2012-4-4 |
Journal Detail:
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Title: Biometrics Volume: - ISSN: 1541-0420 ISO Abbreviation: - Publication Date: 2012 Apr |
Date Detail:
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Created Date: 2012-4-4 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0370625 Medline TA: Biometrics Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
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© 2012, International Biometric Society. |
Affiliation:
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Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109-2029, U.S.A. email: mzhangst@umich.edu. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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