Lehrfach Variationsstatistik, Christian-Albrechts-University of Kiel, Kiel, Germany.
Institute of Biostatistics, Leibniz University Hannover, Hannover, Germany.
Stat Med. 2018 Feb 28;37(5):710-721. doi: 10.1002/sim.7542. Epub 2017 Nov 6.
We present an extension of multiple contrast tests for multiple endpoints to the case of missing values. The endpoints are assumed to be normally distributed and correlated and to have equal covariance matrices for the different treatments. Different multivariate t distributions will be applied, differing in endpoint-specific degrees of freedom. In contrast to competing methods, the familywise error type I is maintained in the strong sense in an admissible range, and the problem of different marginal errors type I is avoided. The information of all observations is exploited, thereby enabling a gain in power compared with a complete case analysis.
我们将多终点多重对比检验的扩展应用于缺失值情况。假设终点正态分布且相关,且不同处理的协方差矩阵相等。将应用不同的多元 t 分布,其在特定终点的自由度不同。与竞争方法相比,在可接受范围内,强烈意义上维持了一类错误的Ⅰ型,且避免了不同边缘错误的Ⅰ型。利用了所有观察值的信息,从而与完全案例分析相比获得了更高的功效。