Asafu-Adjei Josephine K, Sampson Allan R
Department of Biostatistics, School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Biostatistics. 2018 Jan 1;19(1):42-53. doi: 10.1093/biostatistics/kxx015.
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.
在比较多个诊断组的研究中，可以针对某些特征对受试者进行测量，并且可以使用分类树来确定其中哪些特征最能表征组间差异。然而，也可以针对其他协变量对受试者进行测量，这些协变量表征组间差异的能力并无意义或不令人感兴趣，但仍可能对所检查的特征产生影响。因此，对协变量对这些特征的影响进行调整很重要。我们提出了一种新的半参数方法，用于在基于感兴趣的特征构建分类树时调整协变量效应，该方法易于实现。给出了一个应用于死后脑组织数据的示例，以比较精神分裂症患者与正常对照者的神经生物学特征。我们还使用模拟研究评估了我们方法的性能。
