1 Division of Clinical and Translational Sciences, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
2 Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Stat Methods Med Res. 2019 Apr;28(4):1170-1187. doi: 10.1177/0962280217751518. Epub 2018 Jan 9.
We propose a nonparametric test for interactions when we are concerned with investigation of the simultaneous effects of two or more factors in a median regression model with right censored survival data. Our approach is developed to detect interaction in special situations, when the covariates have a finite number of levels with a limited number of observations in each level, and it allows varying levels of variance and censorship at different levels of the covariates. Through simulation studies, we compare the power of detecting an interaction between the study group variable and a covariate using our proposed procedure with that of the Cox Proportional Hazard (PH) model and censored quantile regression model. We also assess the impact of censoring rate and type on the standard error of the estimators of parameters. Finally, we illustrate application of our proposed method to real life data from Prospective Observational Multicenter Major Trauma Transfusion (PROMMTT) study to test an interaction effect between type of injury and study sites using median time for a trauma patient to receive three units of red blood cells. The results from simulation studies indicate that our procedure performs better than both Cox PH model and censored quantile regression model based on statistical power for detecting the interaction, especially when the number of observations is small. It is also relatively less sensitive to censoring rates or even the presence of conditionally independent censoring that is conditional on the levels of covariates.
当我们关注在右删失生存数据的中位数回归模型中两个或多个因素的同时影响时，我们提出了一种用于交互作用的非参数检验。我们的方法是为了在特殊情况下检测交互作用而开发的，当协变量具有有限数量的水平并且每个水平的观测值数量有限时，并且它允许在协变量的不同水平上具有不同的方差和删失程度。通过模拟研究，我们比较了使用我们提出的程序检测研究组变量和协变量之间交互作用的功效与 Cox 比例风险（PH）模型和删失分位数回归模型的功效。我们还评估了删失率和类型对参数估计标准误差的影响。最后，我们通过 Prospective Observational Multicenter Major Trauma Transfusion（PROMMTT）研究的真实生活数据说明了我们提出的方法的应用，以测试损伤类型和研究地点之间的交互作用效应，使用创伤患者接受三个单位红细胞的中位数时间。模拟研究的结果表明，我们的程序在检测交互作用的统计功效方面优于 Cox PH 模型和删失分位数回归模型，尤其是在观测值数量较少的情况下。它对删失率也相对不敏感，甚至对条件独立删失的存在也不敏感，条件独立删失是基于协变量水平的。
