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Adaptive sampling in two‐phase designs: a biomarker study for progression in arthritis
Author(s) -
McIsaac Michael A.,
Cook Richard J.
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6523
Subject(s) - estimator , computer science , sampling (signal processing) , exploit , adaptive sampling , optimal design , sample size determination , phase (matter) , biomarker , resource allocation , statistics , data mining , machine learning , mathematics , monte carlo method , biochemistry , chemistry , computer security , organic chemistry , filter (signal processing) , computer vision , computer network
Response‐dependent two‐phase designs are used increasingly often in epidemiological studies to ensure sampling strategies offer good statistical efficiency while working within resource constraints. Optimal response‐dependent two‐phase designs are difficult to implement, however, as they require specification of unknown parameters. We propose adaptive two‐phase designs that exploit information from an internal pilot study to approximate the optimal sampling scheme for an analysis based on mean score estimating equations. The frequency properties of estimators arising from this design are assessed through simulation, and they are shown to be similar to those from optimal designs. The design procedure is then illustrated through application to a motivating biomarker study in an ongoing rheumatology research program. Copyright © 2015 © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.