Oh Jung Hun, Thor Maria, Olsson Caroline, Skokic Viktor, Jörnsten Rebecka, Alsadius David, Pettersson Niclas, Steineck Gunnar, Deasy Joseph O
Jung Hun Oh, PhD, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1250 First Avenue, New York, NY 10065, USA, E-mail:
Methods Inf Med. 2016 Oct 17;55(5):431-439. doi: 10.3414/ME16-01-0035. Epub 2016 Sep 2.
In the field of radiation oncology, the use of extensive patient reported outcomes is increasingly common to measure adverse side effects after radiotherapy in cancer patients. Factor analysis has the potential to identify an optimal number of latent factors (i.e., symptom groups). However, the ultimate goal of treatment response modeling is to understand the relationship between treatment variables such as radiation dose and symptom groups resulting from FA. Hence, it is crucial to identify clinically more relevant symptom groups and improved response variables from those symptom groups for a quantitative analysis.
The goal of this study is to design a computational method for finding clinically relevant symptom groups from PROs and to test associations between symptom groups and radiation dose.
We propose a novel approach where exploratory factor analysis is followed by confirmatory factor analysis to determine the relevant number of symptom groups. We also propose to use a combination of symptoms in a symptom group identified as a new response variable in linear regression analysis to investigate the relationship between the symptom group and dose-volume variables.
We analyzed patient-reported gastrointestinal symptom profiles from 3 datasets in prostate cancer patients treated with radiotherapy. The final structural model of each dataset was validated using the other two datasets and compared to four other existing FA methods. Our systematic EFA-CFA approach provided clinically more relevant solutions than other methods, resulting in new clinically relevant outcome variables that enabled a quantitative analysis. As a result, statistically significant correlations were found between some dose-volume variables to relevant anatomic structures and symptom groups identified by FA.
Our proposed method can aid in the process of understanding PROs and provide a basis for improving our understanding of radiation-induced side effects.
在放射肿瘤学领域，使用大量患者报告结局来衡量癌症患者放疗后的不良副作用越来越普遍。因子分析有潜力识别潜在因子（即症状组）的最佳数量。然而，治疗反应建模的最终目标是了解诸如辐射剂量等治疗变量与因子分析产生的症状组之间的关系。因此，识别临床上更相关的症状组并从这些症状组中改进反应变量以进行定量分析至关重要。
本研究的目标是设计一种计算方法，用于从患者报告结局中找到临床上相关的症状组，并测试症状组与辐射剂量之间的关联。
我们提出一种新颖的方法，即先进行探索性因子分析，然后进行验证性因子分析以确定相关症状组的数量。我们还建议在症状组中使用症状组合，将其确定为线性回归分析中的新反应变量，以研究症状组与剂量 - 体积变量之间的关系。
我们分析了接受放疗的前列腺癌患者的3个数据集中患者报告的胃肠道症状概况。每个数据集的最终结构模型使用其他两个数据集进行验证，并与其他四种现有的因子分析方法进行比较。我们的系统探索性因子分析 - 验证性因子分析方法比其他方法提供了临床上更相关的解决方案，产生了新的临床上相关的结局变量，从而能够进行定量分析。结果，在一些与相关解剖结构的剂量 - 体积变量和因子分析确定的症状组之间发现了具有统计学意义的相关性。
我们提出的方法有助于理解患者报告结局的过程，并为增进我们对辐射诱导副作用的理解提供基础。
