Biomarker evaluation under imperfect nested case-control design

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

The nested case-control (NCC) design has been widely adopted as a cost-effective sampling design for biomarker research. Under the NCC design, markers are only measured for the NCC subcohort consisting of all cases and a fraction of the controls selected randomly from the matched risk sets of the cases. Robust methods for evaluating prediction performance of risk models have been derived under the inverse probability weighting framework. The probabilities of samples being included in the NCC cohort can be calculated based on the study design ``a previous study'' or estimated non-parametrically ``a previous study''. Neither strategy works well due to model mis-specification and the curse of dimensionality in practical settings where the sampling does not entirely follow the study design or depends on many factors. In this paper, we propose an alternative strategy to estimate the sampling probabilities based on a varying coefficient model, which attains a balance between robustness and the curse of dimensionality. The complex correlation structure induced by repeated finite risk set sampling makes the standard resampling procedure for variance estimation fail. We propose a perturbation resampling procedure that provides valid interval estimation for the proposed estimators. Simulation studies show that the proposed method performs well in finite samples. We apply the proposed method to the Nurses' Health Study II to develop and evaluate prediction models using clinical biomarkers for cardiovascular risk.

OriginalsprogEngelsk
TidsskriftStatistics in Medicine
Vol/bind40
Udgave nummer18
Sider (fra-til)4035-4052
Antal sider18
ISSN0277-6715
DOI
StatusUdgivet - 2021
Eksternt udgivetJa

Bibliografisk note

Funding Information:
This research were funded by U01CA86368, R01CA236558, and R01HL095964 awarded by the National Institutes of Health.

Publisher Copyright:
© 2021 John Wiley & Sons Ltd.

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