Optimal auxiliary‐covariate‐based two‐phase sampling design for semiparametric efficient estimation of a mean or mean difference, with application to clinical trials
Identifieur interne : 000397 ( Istex/Curation ); précédent : 000396; suivant : 000398Optimal auxiliary‐covariate‐based two‐phase sampling design for semiparametric efficient estimation of a mean or mean difference, with application to clinical trials
Auteurs : Peter B. Gilbert [États-Unis] ; Xuesong Yu [États-Unis] ; Andrea Rotnitzky [Argentine, États-Unis]Source :
- Statistics in Medicine [ 0277-6715 ] ; 2014-03-15.
Abstract
To address the objective in a clinical trial to estimate the mean or mean difference of an expensive endpoint Y, one approach employs a two‐phase sampling design, wherein inexpensive auxiliary variables W predictive of Y are measured in everyone, Y is measured in a random sample, and the semiparametric efficient estimator is applied. This approach is made efficient by specifying the phase two selection probabilities as optimal functions of the auxiliary variables and measurement costs. While this approach is familiar to survey samplers, it apparently has seldom been used in clinical trials, and several novel results practicable for clinical trials are developed. We perform simulations to identify settings where the optimal approach significantly improves efficiency compared to approaches in current practice. We provide proofs and R code. The optimality results are developed to design an HIV vaccine trial, with objective to compare the mean ‘importance‐weighted’ breadth (Y) of the T‐cell response between randomized vaccine groups. The trial collects an auxiliary response (W) highly predictive of Y and measures Y in the optimal subset. We show that the optimal design‐estimation approach can confer anywhere between absent and large efficiency gain (up to 24 % in the examples) compared to the approach with the same efficient estimator but simple random sampling, where greater variability in the cost‐standardized conditional variance of Y given W yields greater efficiency gains. Accurate estimation of E[Y | W] is important for realizing the efficiency gain, which is aided by an ample phase two sample and by using a robust fitting method. Copyright © 2013 John Wiley & Sons, Ltd.
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DOI: 10.1002/sim.6006
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Gilbert, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave North, PO Box 19024, Seattle, WA 98109, U.S.A.E‐mail:</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: pgilbert@scharp.org</mods:affiliation></affiliation></author><author><name sortKey="Yu, Xuesong" sort="Yu, Xuesong" uniqKey="Yu X" first="Xuesong" last="Yu">Xuesong Yu</name><affiliation wicri:level="1"><mods:affiliation>Vaccine Infectious Disease Division, Fred Hutchinson Cancer Research Center, WA 98109, Seattle, U.S.A.</mods:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Vaccine Infectious Disease Division, Fred Hutchinson Cancer Research Center, WA 98109, Seattle</wicri:regionArea></affiliation></author><author><name sortKey="Rotnitzky, Andrea" sort="Rotnitzky, Andrea" uniqKey="Rotnitzky A" first="Andrea" last="Rotnitzky">Andrea Rotnitzky</name><affiliation wicri:level="1"><mods:affiliation>Department of Economics, Universidad Di Tella, Buenos Aires, Argentina</mods:affiliation><country xml:lang="fr">Argentine</country><wicri:regionArea>Department of Economics, Universidad Di Tella, Buenos Aires</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>Department of Biostatistics, Harvard School of Public Health, MA 02115, Boston, U.S.A.</mods:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biostatistics, Harvard School of Public Health, MA 02115, Boston</wicri:regionArea></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Statistics in Medicine</title><title level="j" type="alt">STATISTICS IN MEDICINE</title><idno type="ISSN">0277-6715</idno><idno type="eISSN">1097-0258</idno><imprint><biblScope unit="vol">33</biblScope><biblScope unit="issue">6</biblScope><biblScope unit="page" from="901">901</biblScope><biblScope unit="page" to="917">917</biblScope><biblScope unit="page-count">17</biblScope><date type="published" when="2014-03-15">2014-03-15</date></imprint><idno type="ISSN">0277-6715</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0277-6715</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract">To address the objective in a clinical trial to estimate the mean or mean difference of an expensive endpoint Y, one approach employs a two‐phase sampling design, wherein inexpensive auxiliary variables W predictive of Y are measured in everyone, Y is measured in a random sample, and the semiparametric efficient estimator is applied. This approach is made efficient by specifying the phase two selection probabilities as optimal functions of the auxiliary variables and measurement costs. While this approach is familiar to survey samplers, it apparently has seldom been used in clinical trials, and several novel results practicable for clinical trials are developed. We perform simulations to identify settings where the optimal approach significantly improves efficiency compared to approaches in current practice. We provide proofs and R code. The optimality results are developed to design an HIV vaccine trial, with objective to compare the mean ‘importance‐weighted’ breadth (Y) of the T‐cell response between randomized vaccine groups. The trial collects an auxiliary response (W) highly predictive of Y and measures Y in the optimal subset. We show that the optimal design‐estimation approach can confer anywhere between absent and large efficiency gain (up to 24 % in the examples) compared to the approach with the same efficient estimator but simple random sampling, where greater variability in the cost‐standardized conditional variance of Y given W yields greater efficiency gains. Accurate estimation of E[Y | W] is important for realizing the efficiency gain, which is aided by an ample phase two sample and by using a robust fitting method. Copyright © 2013 John Wiley & Sons, Ltd.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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