A pretest for using logrank or Wilcoxon in the two-sample problem

In a two-sample location-scalemodelwith censored data, the logrank test is asymptotically efficient when the error distribution is extreme minimum value. On the other hand, the Wilcoxon test is asymptotically efficient when the error distribution is logistic. We propose a pretest for choosing between logrank and Wilcoxon by determining if the error distribution is closer to extrememinimum value or logistic. This adaptive test is compared with the logrank and Wilcoxon tests through simulation. © 2011 Elsevier B.V. All rights reserved.