Response-dependent two-phase sampling designs for biomarker studies
Abstract
Two-phase sampling designs are developed and investigated for use in the context of a rheumatology
study where interest lies in the association between a biomarker with an expensive assay and
disease progression. We derive optimal phase-II stratum-specific sampling probabilities for analyses
from parametric maximum likelihood (ML), mean score (MS), inverse probability weighted
(IPW), and augmented IPW (AIPW) estimating equations. The easy-to-implement optimally efficient
design for the MS estimator is found to be asymptotically optimal for the IPW and AIPW
estimators we consider, and is shown to result in efficiency gains over balanced and simple random
sampling even when analyses are likelihood-based. We further demonstrate the robustness
of this optimal design and show that it results in very efficient estimation even when the model or
parameters used in its derivation are misspecified.
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Cite this version of the work
Richard J. Cook, Michael A. McIsaac
(2014).
Response-dependent two-phase sampling designs for biomarker studies. UWSpace.
http://hdl.handle.net/10012/10232
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