Koopmeiners Joseph S, Feng Ziding
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, 55455, U.S.A.
Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, 77230, U.S.A.
Stat Med. 2016 Apr 15;35(8):1267-80. doi: 10.1002/sim.6790. Epub 2015 Nov 4.
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.
序贯分组检验程序已被提出作为一种在生物标志物验证研究中节省资源的方法。此前，我们推导了序贯经验阳性预测值（PPV）和阴性预测值（NPV）曲线的渐近性质，这些曲线总结了在病例对照抽样下连续标志物的预测准确性。这种方法的一个局限性在于，患病率无法从病例对照研究中估计出来，必须假定为已知。在本文中，我们考虑对患病率未知的连续生物标志物的预测准确性进行序贯分组检验。首先，我们在必须估计患病率而非在病例对照研究中假定已知患病率的情况下，为序贯经验PPV和NPV曲线建立渐近理论。然后，我们讨论如何将我们的结果与标准序贯分组方法相结合，以开发序贯分组检验程序以及PPV和NPV曲线的偏差调整估计量。通过模拟评估了所提出的序贯分组检验程序和估计量的小样本性质，并且我们在一项验证前列腺癌新型生物标志物的研究背景下阐述了我们的方法。
