Cancer-related fatigue in breast cancer patients: factor mixture models with continuous non-normal distributions
Cancer-related fatigue in breast cancer patients: factor mixture models with continuous non-normal distributions
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DOI：
10.1007/s11136-014-0731-7
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发表时间：
2014-12-01
影响因子：
3.5
通讯作者：
Cheung, Irene K. M.
中科院分区：
文献类型：
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作者：
Ho, Rainbow T. H.;Fong, Ted C. T.;Cheung, Irene K. M.
疲劳是乳腺癌患者最普遍和最显着的症状之一。本研究旨在使用创新的非正态混合模型来调查患者疲劳症状的潜在人群异质性。197 名乳腺癌患者样本完成了简短的疲劳清单和其他癌症症状测量。使用正态分布、t 分布、偏斜正态分布和偏斜 t 分布对非正态因素混合模型进行了分析和比较。潜在类别数量的选择基于贝叶斯信息准则（BIC）。通过使用逐步远端结果方法比较人口统计资料、临床特征和癌症症状来验证确定的类别。观察到的疲劳项目显示出轻微的偏度，但有明显的负峰度。使用正态分布的因子混合模型指向 3 类解。 t 分布混合模型显示 2 类模型的 BIC 最低。恢复的班级（52.5%）表现出中等严重程度（项目平均值 = 2.8-3.2）和低干扰（项目平均值 = 1.1-1.9）。精疲力尽的类别 (47.5%) 显示出高水平的疲劳严重程度和干扰（项目平均值 = 5.8-6.6）。与恢复的类别相比，精疲力尽的类别报告明显更高的感知压力、焦虑、抑郁、疼痛、睡眠障碍和较低的生活质量。非正态因素混合模型表明患者的疲劳症状有两个不同的亚组。疲惫不堪且症状加剧的群体的存在要求对症状进行主动评估，并针对该亚群制定量身定制的干预措施。
Fatigue is one of the most prevalent and significant symptoms experienced by breast cancer patients. This study aimed to investigate potential population heterogeneity in fatigue symptoms of the patients using the innovative non-normal mixture modeling.A sample of 197 breast cancer patients completed the brief fatigue inventory and other measures on cancer symptoms. Non-normal factor mixture models were analyzed and compared using the normal, t, skew-normal, and skew-t distributions. Selection of the number of latent classes was based on the Bayesian information criterion (BIC). The identified classes were validated by comparing their demographic profiles, clinical characteristics, and cancer symptoms using a stepwise distal outcome approach.The observed fatigue items displayed slight skewness but evident negative kurtosis. Factor mixture models using the normal distribution pointed to a 3-class solution. The t distribution mixture models showed the lowest BIC for the 2-class model. The restored class (52.5 %) exhibited moderate severity (item mean = 2.8-3.2) and low interference (item mean = 1.1-1.9). The exhausted class (47.5 %) displayed high levels of fatigue severity and interference (item mean = 5.8-6.6). Compared to the restored class, the exhausted class reported significantly higher perceived stress, anxiety, depression, pain, sleep disturbance, and lower quality of life.The non-normal factor mixture models suggest two distinct subgroups of patients on their fatigue symptoms. The presence of the exhausted class with exacerbated symptoms calls for a proactive assessment of the symptoms and development of tailored interventions for this subgroup.