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Double Extreme Ranked Set Sampling (DERSS) was first introduced by Samawi (2002) as a modification to the well-known Ranked Set Sampling (RSS) and Extreme Ranked Set Sampling (ERSS). In this article, we provide a modification to DERSS scheme with ranking based on an easy-to-rank baseline auxiliary variable known to be associated with survival time. We show that using the modified DERSS improves the performance of the Accelerated failure time (AFT) survival model and provides a more efficient estimator of the hazard ratio than that based on their counter parts simple random sample (SRS), RSS and ERSS, which results in reducing the sample size required and hence the total cost of the study. Our theoretical and simulation studies show the superiority of using the modified DERSS for AFT survival models compared with using SRS, RSS and ERSS.  A numerical example based on Worcester Heart Attack Study is presented to illustrate the implementation of the DERSS.


Accelerated Failure Time Model Hazards Ratio Double Extreme Ranked Set Sampling Extreme Ranked Set Sampling Survival analysis.

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Author Biographies

Hani Samawi, Department of Biostatistics Georgia Southern University USA

Ph.D. in Biostatistics from the University of Iowa, USA on 1994. Full Professor of Biostatistics at  Yarmouk University until 2006. Currently,  I am  a tenured Full Professor in Biostatistics in Jiann-Ping Hsu college of Public Health College at Georgia Southern university. I served as the Director of the Karl E. Peace Center for Biostatistics form August 2008 to June 2016.  In addition I became a fellow of the Institute for Interdisciplinary STEM Education, Editorial Board member for the Biometrics & Biostatistics International Journal and Associate Editor of Frontiers in Child Health and Human Development.


Amal Helu, The university of Jordan

Associate professor

Herash Rochani, Department of Biostatistics Georgia Southern University USA

Assistance professor