Nonparametric and Semiparametric Group Sequential Methods for Comparing Accuracy of Diagnostic Tests
Comparison of the accuracy of two diagnostic tests using the receiver operating characteristic (ROC) curves from two diagnostic tests has been typically conducted using fixed sample designs. On the other hand, the human experimentation inherent in a comparison of diagnostic modalities argues for periodic monitoring of the accruing data to address many issues related to the ethics and efficiency of the medical study. To date, very little research has been done in the use of sequential sampling plans for comparative ROC studies, even when these studies may use expensive and unsafe diagnostic procedures. In this paper, we propose a nonparametric group sequential design plan. The nonparametric sequential method adapts a nonparametric family of weighted area under the ROC curve statistics (Wieand et al., Biometrika, 76: 585-592, 1989) and a group sequential sampling plan. We illustrate the implementation of this nonparametric approach for sequentially comparing ROC curves in the context of diagnostic screening for non-small cell lung cancer. We also describe a semiparametric sequential method based on proportional hazard models. We compare the statistical properties of the nonparametric approach to alternative semiparametric and parametric analyses in simulation studies. The results show the nonparametric approach is robust to model misspecification and has excellent finite sample performance.
Tang, Liansheng; Emerson, Scott S.; and Zhou, Xiao-Hua, "Nonparametric and Semiparametric Group Sequential Methods for Comparing Accuracy of Diagnostic Tests" (October 2007). UW Biostatistics Working Paper Series. Working Paper 316.