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Using Cox regression to develop linear rank tests with zero‐inflated clustered data
The Journal of the Royal Statistical Society: Series C (Applied Statistics) ( IF 1.6 ) Pub Date : 2020-02-03 , DOI: 10.1111/rssc.12396
Stuart R. Lipsitz 1 , Garrett M. Fitzmaurice 2 , Debajyoti Sinha 3 , Alexander P. Cole 1 , Christian P. Meyer 4 , Quoc‐Dien Trinh 1
Affiliation  

Zero‐inflated data arise in many fields of study. When comparing zero‐inflated data between two groups with independent subjects, a 2 degree‐of‐freedom test has been developed, which is the sum of a 1 degree‐of‐freedom Pearson χ2‐test for the 2×2 table of group versus dichotomized outcome (0,>0) and a 1 degree‐of‐freedom Wilcoxon rank sum test for the values of the outcome ‘>0’. Here, we extend this 2 degrees‐of‐freedom test to clustered data settings. We first propose the use of an estimating equations score statistic from a time‐varying weighted Cox regression model under naive independence, with a robust sandwich variance estimator to account for clustering. Since our proposed test statistics can be put in the framework of a Cox model, to gain efficiency over naive independence, we apply a generalized estimating equations Cox model with a non‐independence ‘working correlation’ between observations in a cluster. The methods proposed are applied to a General Social Survey study of days with mental health problems in a month, in which 52.3% of subjects report that they have no days with problems: a zero‐inflated outcome. A simulation study is used to compare our proposed test statistics with previously proposed zero‐inflated test statistics.

中文翻译:

使用Cox回归开发零膨胀聚类数据的线性等级检验

零膨胀数据出现在许多研究领域。当两个基团与独立的受试者之间进行比较的零膨胀数据,2度的自由度的测试已经被开发出来,这是一个1度的自由度皮尔逊的总和χ 2 -test为组的2×2表将结果二分(0,> 0)和1自由度Wilcoxon秩和检验以得出结果'> 0'的值。在这里,我们将此2自由度测试扩展到群集数据设置。我们首先提出在朴素的独立性下使用时变加权Cox回归模型的估计方程得分统计量,并使用健壮的三明治方差估计量来说明聚类。由于我们建议的测试统计数据可以放在Cox模型的框架中,以提高天真的独立性的效率,因此我们在群集中使用观测值之间具有非独立“工作相关性”的广义估计方程Cox模型。提议的方法被用于一个月内有精神健康问题的日子的一般社会调查研究,其中52.3%的受试者报告他们没有精神问题的日子:零膨胀的结果。通过仿真研究将我们提出的测试统计数据与先前提出的零膨胀测试统计数据进行比较。
更新日期:2020-04-23
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