Department of Civil Environmental and Architectural Engineering, University of Padova, Padua, Italy.
Department of Mathematics, University of Salzburg, Salzburg, Austria.
Stat Methods Med Res. 2020 Jan;29(1):258-271. doi: 10.1177/0962280219832225. Epub 2019 Feb 25.
Very often, data collected in medical research are characterized by censored observations and/or data with mass on the value zero. This happens for example when some measurements fall below the detection limits of the specific instrument used. This type of left censored observations is called "nondetects". Such a situation of an excessive number of zeros in a data set is also referred to as zero-inflated data. In the present work, we aim at comparing different multivariate permutation procedures in two-sample testing for data with nondetects. The effect of censoring is investigated with regard to the different values that may be attributed to nondetected values, both under the null hypothesis and under alternative. We motivate the problem using data from allergy research.
在医学研究中，经常会遇到有删失观测值和/或数据中大量值为零的情况。例如，当某些测量值低于特定仪器的检测限时，就会出现这种情况。这种类型的左删失观测值被称为“未检出”。当数据集中有过多的零时，这种情况也被称为“零膨胀数据”。在本工作中，我们旨在比较在含有未检出值的数据的两样本检验中不同的多元置换检验程序。研究了删失的影响，考虑了在零假设和备择假设下，未检出值可能具有的不同值。我们使用过敏研究的数据来提出这个问题。
