Abstract:
:Group sequential testing procedures have been proposed as an approach to conserving resources in biomarker validation studies. Previously, we derived the asymptotic properties of the sequential empirical positive predictive value (PPV) and negative predictive value (NPV) curves, which summarize the predictive accuracy of a continuous marker, under case-control sampling. A limitation of this approach is that the prevalence cannot be estimated from a case-control study and must be assumed known. In this paper, we consider group sequential testing of the predictive accuracy of a continuous biomarker with unknown prevalence. First, we develop asymptotic theory for the sequential empirical PPV and NPV curves when the prevalence must be estimated, rather than assumed known in a case-control study. We then discuss how our results can be combined with standard group sequential methods to develop group sequential testing procedures and bias-adjusted estimators for the PPV and NPV curve. The small sample properties of the proposed group sequential testing procedures and estimators are evaluated by simulation, and we illustrate our approach in the context of a study to validate a novel biomarker for prostate cancer.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Koopmeiners JS,Feng Zdoi
10.1002/sim.6790subject
Has Abstractpub_date
2016-04-15 00:00:00pages
1267-80issue
8eissn
0277-6715issn
1097-0258journal_volume
35pub_type
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