Institute of Statistics, Ulm University, Ulm, Germany.
Institute of Biometry and Clinical Epidemiology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Stat Med. 2019 Jul 30;38(17):3243-3255. doi: 10.1002/sim.8178. Epub 2019 May 17.
We consider statistical procedures for hypothesis testing of real valued functionals of matched pairs with missing values. In order to improve the accuracy of existing methods, we propose a novel multiplication combination procedure. Dividing the observed data into dependent (completely observed) pairs and independent (incompletely observed) components, it is based on combining separate results of adequate tests for the two sub data sets. Our methods can be applied for parametric as well as semiparametric and nonparametric models and make use of all available data. In particular, the approaches are flexible and can be used to test different hypotheses in various models of interest. This is exemplified by a detailed study of mean- as well as rank-based approaches under different missingness mechanisms with different amount of missing data. Extensive simulations show that in most considered situations, the proposed procedures are more accurate than existing competitors particularly for the nonparametric Behrens-Fisher problem. A real data set illustrates the application of the methods.
我们考虑了用于具有缺失值的配对观察值的实值函数的假设检验的统计程序。为了提高现有方法的准确性，我们提出了一种新的乘法组合程序。将观察数据分为相关（完全观察）对和独立（不完全观察）组件，它基于对两个子数据集的适当测试的单独结果进行组合。我们的方法可用于参数、半参数和非参数模型，并利用所有可用数据。特别是，这些方法灵活，可以用于在各种感兴趣的模型中测试不同的假设。这通过对不同缺失机制下不同缺失数据量的均值和基于秩的方法的详细研究来说明。广泛的模拟表明，在大多数考虑的情况下，与现有竞争对手相比，所提出的程序特别是对于非参数 Behrens-Fisher 问题更准确。一个真实数据集说明了该方法的应用。
