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首页> 外文期刊>Journal of near infrared spectroscopy >NOPAPROD non-parametric testing on projections from multivariate data. Applications to near infrared spectroscopy in clinical studies
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NOPAPROD non-parametric testing on projections from multivariate data. Applications to near infrared spectroscopy in clinical studies

机译：NOPAPROD对来自多元数据的投影进行非参数测试。近红外光谱在临床研究中的应用

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摘要

Clinical studies may be carried out using non-invasively collected near infrared spectra of patient skin. Two problems encountered are: (1) data reduction to go from thousands of wavelengths to some clinically relevant estimator and (2) getting statistical significance from noisy data with sometimes very skewed distributions. The problem of data reduction can usually be solved by principal component analysis to get a few meaningful components. In the space spanned by these components, a direction of discrimination may have to be found, typically discrimination between treated and control. A visual difference in a score plot is often not enough; statistical significance has to be demonstrated. Once a univariate estimator is found, non-parametric testing can show significant differences, even if the data are noisy and have an unknown and skewed distribution. The NOPRAPOD method combines the actions of finding a direction in a reduced data space and performing the non-parametric significance testing by producing a disk of significance. Two examples are included. Example one is from a study of diabetes-related neuropathy where it is shown that significant differences show up in the NIR spectra. Example two is from a study of post-operative radiation treatment of breast cancer patients, where it is shown that radiation effects (erythema) and the effect of lotion can be determined with an indication of significance from the NIR spectra.
机译：可以使用无创收集的患者皮肤近红外光谱进行临床研究。遇到两个问题：（1）将数据从数千个波长简化为某种临床相关的估计量;（2）从有时分布非常偏斜的嘈杂数据中获得统计意义。数据减少的问题通常可以通过主成分分析来解决，以获得一些有意义的成分。在这些组件所跨越的空间中，可能必须找到区分的方向，通常是已处理和对照之间的区分。分数图中的视觉差异通常是不够的。有统计学意义。一旦发现单变量估计量，即使数据嘈杂且分布未知且偏斜，非参数检验也可能显示出显着差异。 NOPRAPOD方法结合了以下操作：在减少的数据空间中查找方向，并通过生成重要性盘来执行非参数重要性测试。包括两个示例。实例一来自对糖尿病相关的神经病的研究，其中显示出NIR光谱中显示出显着差异。实施例二来自对乳腺癌患者的术后放射治疗的研究，其中表明可以确定放射作用（红斑）和洗液的作用，并从NIR光谱中得出有意义的指示。

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