Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas School of Public Health at Houston, Houston, TX 77030, United States of America.
Division of Clinical and Translational Sciences, Department of Internal Medicine, McGovern Medical School; University of Texas Health Science Center at Houston, Houston, TX 77030, United States of America.
PLoS One. 2018 May 17;13(5):e0197295. doi: 10.1371/journal.pone.0197295. eCollection 2018.
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.
我们提出了一种针对右删失数据的多个独立样本的中位数生存时间的非参数收缩估计器，它结合了样本和假设信息，以提高效率。我们通过广泛的模拟研究比较了所提出的收缩估计程序与无约束估计和组合估计的效率。我们的结果表明，这些估计器的性能取决于中位数同质性的强度。当同质性成立时，组合估计是最有效的估计器。然而，当同质性失效时，它变得不一致。另一方面，所提出的收缩估计仍然有效。它的效率随着生存中位数的相等性的偏差而降低，但有望与无约束估计一样好或更好。我们的模拟研究还表明，所提出的收缩估计对适度的删失水平具有稳健性。我们展示了这些方法在 Prospective Observational Multi-center Major Trauma Transfusion（PROMMTT）研究中估计创伤患者接受红细胞的中位数时间的应用。
