Nonparametric Estimation of Median Survival Times with Applications to Multisite or Multicenter Studies

• Activity Number: 170 - Nonparametric Methods for Longitudinal and Survival Data
• Type: Contributed
• Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Mohammad Rahbar* and Sangbum Choi and Chuan Hong and Liang Zhu and Sangchoon Jeon and Joseph C. Gardiner
• Companies: University of Texas Health Science Center at Houston and Korea University and Havard University T. Chan School of Public Health and University of Texas Health Science Center at Houston and Yale University and Michigan State University
• Keywords: Median survival time ; Nonparametric ; Shrinkage estimation ; PROMMTT

Abstract

We propose a nonparametric shrinkage estimator for the median survival times from several independent samples of right-censored data, which combines the samples and hypothesis information to improve the efficiency. We compare efficiency of the proposed shrinkage estimation procedure to unrestricted estimator and combined estimator asymptotically and through extensive simulation studies. Our results indicate that performance of these estimators depends on the strength of homogeneity of the medians. When homogeneity holds, the combined estimator is the most efficient estimator. However, it becomes inconsistent when homogeneity fails. On the other hand, the proposed shrinkage estimator remains to be efficient. Its efficiency decreases as the equality of the survival medians is deviated, but is expected to be as good or equal to the unrestricted estimator. Our simulation studies also indicate that the proposed shrinkage estimator is robust to moderate levels of censoring. We demonstrate application of these methods to estimating median time for trauma patients to receive red blood cells in the Prospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study.