Nonparametric and Semiparametric Group Sequential Methods for Comparing Accuracy of Diagnostic Tests
Article first published online: 27 MAR 2008
© 2008, The International Biometric Society
Volume 64, Issue 4, pages 1137–1145, December 2008
How to Cite
Tang, L., Emerson, S. S. and Zhou, X.-H. (2008), Nonparametric and Semiparametric Group Sequential Methods for Comparing Accuracy of Diagnostic Tests. Biometrics, 64: 1137–1145. doi: 10.1111/j.1541-0420.2008.01000.x
- Issue published online: 24 NOV 2008
- Article first published online: 27 MAR 2008
- Received June 2007. Revised October 2007. Accepted October 2007.
- Diagnostic accuracy;
- Proportional hazard model;
- Weighted AUC
Summary 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 on the use of sequential sampling plans for comparative ROC studies, even when these studies may use expensive and unsafe diagnostic procedures. In this article 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., 1989, Biometrika 76, 585–592) 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 nonsmall-cell lung cancer. We also describe a semiparametric sequential method based on proportional hazard models. We compare the statistical properties of the nonparametric approach with 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.