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Nonparametric estimation of median survival times with applications to multi-site or multi-center studies

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  • Mohammad H Rahbar
  • Sangbum Choi
  • Chuan Hong
  • Liang Zhu
  • Sangchoon Jeon
  • Joseph C Gardiner

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 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 efficient. Its efficiency decreases as the equality of the survival medians is deviated, but is expected to be as good as 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.

Suggested Citation

  • Mohammad H Rahbar & Sangbum Choi & Chuan Hong & Liang Zhu & Sangchoon Jeon & Joseph C Gardiner, 2018. "Nonparametric estimation of median survival times with applications to multi-site or multi-center studies," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-15, May.
  • Handle: RePEc:plo:pone00:0197295
    DOI: 10.1371/journal.pone.0197295
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    References listed on IDEAS

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    1. Rahbar, Mohammad H. & Gardiner, Joseph C., 1995. "Nonparametric estimation of regression parameters from censored data with a discrete covariate," Statistics & Probability Letters, Elsevier, vol. 24(1), pages 13-20, July.
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