Two-phase biomarker studies for disease progression with multiple registries

We consider the design and analysis of two-phase studies of the association between an expensive biomarker and disease progression when phase I data are obtained by pooling registries having different outcome-dependent recruitment schemes. We utilize two analysis methods, namely maximum-likelihood a...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society Series C: Applied Statistics 2024-11, Vol.73 (5), p.1111-1133
Main Authors: Mao, Fangya, Cook, Richard J
Format: Article
Language:English
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Summary:We consider the design and analysis of two-phase studies of the association between an expensive biomarker and disease progression when phase I data are obtained by pooling registries having different outcome-dependent recruitment schemes. We utilize two analysis methods, namely maximum-likelihood and inverse probability weighting (IPW), to handle missing covariates arising from a two-phase design. In the likelihood framework, we derive a class of residual-dependent designs for phase II sub-sampling from an observed data likelihood accounting for the phase I sampling plans used by the different registries. In the IPW approach, we derive and evaluate optimal stratified designs that approximate Neyman allocation. Simulation studies and an application to a motivating example demonstrate the finite sample improvements from the proposed designs over simple random sampling and standard stratified sampling schemes.
ISSN:0035-9254
1467-9876
DOI:10.1093/jrsssc/qlae028