Covariate adjusted classification trees
Covariate adjusted classification trees
复制标题
DOI：
10.1093/biostatistics/kxx015
复制
发表时间：
2018-01-01
期刊：
影响因子：
2.1
通讯作者：
Sampson, Allan R.
中科院分区：
文献类型：
--
作者：
Asafu-Adjei, Josephine K.;Sampson, Allan R.
在比较几个诊断组的研究中，可以在某些特征上测量受试者，并且分类树可以用于确定其中哪个最能表征组间差异。然而，受试者也可以用其他协变量进行测量，这些协变量表征组差异的能力没有意义或没有意义，但仍可能对检查的特征产生影响。因此，重要的是要调整协变量对这些特征的影响。我们提出了一种新的半参数方法来调整协变量的影响时，构建分类树的基础上的功能的兴趣，是很容易实现的。一个应用程序给出了死后的脑组织数据，比较精神分裂症患者的神经生物学特征与正常对照组。我们还使用模拟研究评估我们的方法的性能。
In studies that compare several diagnostic groups, subjects can be measured on certain features and classification trees can be used to identify which of them best characterize the differences among groups. However, subjects may also be measured on additional covariates whose ability to characterize group differences is not meaningful or of interest, but may still have an impact on the examined features. Therefore, it is important to adjust for the effects of covariates on these features. We present a new semi-parametric approach to adjust for covariate effects when constructing classification trees based on the features of interest that is readily implementable. An application is given for postmortem brain tissue data to compare the neurobiological characteristics of subjects with schizophrenia to those of normal controls. We also evaluate the performance of our approach using a simulation study.