Cook, Richard J.McIsaac, Michael A.2016-01-262016-01-262014http://dx.doi.org/10.1002/cjs.11207http://hdl.handle.net/10012/10232This is the peer reviewed version of the following article: McIsaac, M. A. and Cook, R. J. (2014), Response-dependent two-phase sampling designs for biomarker studies. Can J Statistics, 42: 268–284. doi: 10.1002/cjs.11207, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/cjs.11207/references. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.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.enAsymptotic relative efficiencyaugmented inverse probability weighted estimating functionsinverse probability weightingmaximum likelihood estimationresponse-dependent samplingtwo-phase designArticle