Novel statistical approaches for non-normal censored immunological data: analysis of cytokine and gene expression data.
Novel statistical approaches for non-normal censored immunological data: analysis of cytokine and gene expression data.
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DOI：
10.1371/journal.pone.0046423
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Schaub B
中科院分区：
文献类型：
--
作者：
Ballenberger N;Lluis A;von Mutius E;Illi S;Schaub B
对于一些免疫介导的疾病，未来的免疫分析将变得更加复杂，其中细胞因子和基因表达数据将发挥主要作用。这些数据具有一定的特征，需要复杂的统计分析，如非正态分布和审查策略。此外，复杂和多重的免疫学关系需要调整潜在的混淆和相互作用的影响。我们旨在介绍和应用不同的方法来统计分析非正常缺失细胞因子和基因表达数据。此外，我们评估了一种新的回归方法的性能和准确性，以允许调整协变量和潜在的混淆。对于非正态分布的截尾数据，使用Kaplan-Meier方法或广义Wilcoxon检验等传统方法。为了调整协变量，引入了一种新的秩上Tobit回归方法。通过模拟研究和应用排列和引导的统计实验，评估了该方法在分析非正常缺失细胞因子/基因表达数据方面的性能和准确性。如果协变量的调整是不必要的，传统的统计方法是足够的非正态审查数据。与这些相比较，如果需要额外的调整，Tobit回归是一种有效的方法。其有效性、i型错误率和准确率与经典Tobit回归相当。非正态分布的删节免疫数据需要适当的统计方法。Tobit秩回归符合这些要求，可用于调整协变量和大型复杂免疫学数据集中的潜在混淆。
For several immune-mediated diseases, immunological analysis will become more complex in the future with datasets in which cytokine and gene expression data play a major role. These data have certain characteristics that require sophisticated statistical analysis such as strategies for non-normal distribution and censoring. Additionally, complex and multiple immunological relationships need to be adjusted for potential confounding and interaction effects. We aimed to introduce and apply different methods for statistical analysis of non-normal censored cytokine and gene expression data. Furthermore, we assessed the performance and accuracy of a novel regression approach in order to allow adjusting for covariates and potential confounding. For non-normally distributed censored data traditional means such as the Kaplan-Meier method or the generalized Wilcoxon test are described. In order to adjust for covariates the novel approach named Tobit regression on ranks was introduced. Its performance and accuracy for analysis of non-normal censored cytokine/gene expression data was evaluated by a simulation study and a statistical experiment applying permutation and bootstrapping. If adjustment for covariates is not necessary traditional statistical methods are adequate for non-normal censored data. Comparable with these and appropriate if additional adjustment is required, Tobit regression on ranks is a valid method. Its power, type-I error rate and accuracy were comparable to the classical Tobit regression. Non-normally distributed censored immunological data require appropriate statistical methods. Tobit regression on ranks meets these requirements and can be used for adjustment for covariates and potential confounding in large and complex immunological datasets.
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影响因子：
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