COMPARING TRIALS WITH MULTIPLE OUTCOMES - THE MULTIVARIATE ONE-SIDED HYPOTHESIS WITH UNKNOWN COVARIANCES
COMPARING TRIALS WITH MULTIPLE OUTCOMES - THE MULTIVARIATE ONE-SIDED HYPOTHESIS WITH UNKNOWN COVARIANCES
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DOI：
10.1002/bimj.4710370803
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发表时间：
1995-01-01
影响因子：
1.7
通讯作者：
FRICK, H
中科院分区：
文献类型：
--
作者：
FRICK, H
本文讨论了临床试验中出现的一个问题。待比较的标准和新治疗的结果是多变量正态分布的，具有共同但未知的协方差矩阵。在零假设下，结果的平均值相等，在替代假设下，新治疗被假设为上级，即平均值更大，无需进一步量化。对于已知的协方差矩阵，有各种各样的测试这个问题。如果愿意接受偏差，这些程序中的一些可以扩展到未知协方差的情况。然而，也有一个有效的无偏检验。本文包含了一些数值比较这些不同的程序，并考虑了极大极小无偏检验的性质。
The paper deals with a problem arising for tests in clinical trials. The outcomes of a standard and a new treatment to be compared are multivariate normally distributed with common but unknown covariance matrix. Under the null hypothesis the means of the outcomes are equal, under the alternative the new treatment is assumed to be superior, i.e. the means are larger without further quantification. For known covariance matrix there is a variety of tests for this problem. Some of these procedures can be extended to the case of unknown covariances if one is willing to accept a bias. There is, however, also an efficient unbiased test. The paper contains some numerical comparisons of these different procedures and takes a look on the minimax properties of the unbiased test.