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首页> 外文期刊>OncoTargets and therapy >Using an innovative multiple regression procedure in a cancer population (Part I): detecting and probing relationships of common interacting symptoms (pain, fatigue/weakness, sleep problems) as a strategy to discover influential symptom pairs and clusters
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Using an innovative multiple regression procedure in a cancer population (Part I): detecting and probing relationships of common interacting symptoms (pain, fatigue/weakness, sleep problems) as a strategy to discover influential symptom pairs and clusters

机译:在癌症人群中使用创新的多元回归程序(第I部分):检测和探究常见相互作用症状(疼痛,疲劳/虚弱,睡眠问题)的关系,作为发现有影响力的症状对和症状的策略

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Background: The majority of patients with advanced cancer experience symptom pairs or clusters among pain, fatigue, and insomnia. Improved methods are needed to detect and interpret interactions among symptoms or diesease markers to reveal influential pairs or clusters. In prior work, I developed and validated sequential residual centering (SRC), a method that improves the sensitivity of multiple regression to detect interactions among predictors, by conditioning for multicollinearity (shared variation) among interactions and component predictors.Materials and methods: Using a hypothetical three-way interaction among pain, fatigue, and sleep to predict depressive affect, I derive and explain SRC multiple regression. Subsequently, I estimate raw and SRC multiple regressions using real data for these symptoms from 268 palliative radiation outpatients.Results: Unlike raw regression, SRC reveals that the three-way interaction (pain × fatigue/weakness × sleep problems) is statistically significant. In follow-up analyses, the relationship between pain and depressive affect is aggravated (magnified) within two partial ranges: 1) complete-to-some control over fatigue/weakness when there is complete control over sleep problems (ie, a subset of the pain–fatigue/weakness symptom pair), and 2) no control over fatigue/weakness when there is some-to-no control over sleep problems (ie, a subset of the pain–fatigue/weakness–sleep problems symptom cluster). Otherwise, the relationship weakens (buffering) as control over fatigue/weakness or sleep problems diminishes.Conclusion: By reducing the standard error, SRC unmasks a three-way interaction comprising a symptom pair and cluster. Low-to-moderate levels of the moderator variable for fatigue/weakness magnify the relationship between pain and depressive affect. However, when the comoderator variable for sleep problems accompanies fatigue/weakness, only frequent or unrelenting levels of both symptoms magnify the relationship. These findings suggest that a countervailing mechanism involving depressive affect could account for the effectiveness of a cognitive behavioral intervention to reduce the severity of a pain, fatigue, and sleep disturbance cluster in a previous randomized trial.
机译:背景:大多数晚期癌症患者会经历疼痛,疲劳和失眠的症状对或成群症状。需要改进的方法来检测和解释症状或病酶标记之间的相互作用以揭示有影响的对或簇。在先前的工作中,我开发并验证了顺序残差居中(SRC),这是一种通过对交互作用和预测分量之间的多重共线性(共享变异)进行条件处理来提高多元回归来检测预测变量之间相互关系的敏感性的方法。假设疼痛,疲劳和睡眠之间的三向相互作用预测抑郁症的影响,我得出并解释了SRC多元回归。随后,我使用来自268名姑息放疗门诊患者的这些症状的真实数据来估计原始和SRC多元回归。结果:与原始回归不同,SRC揭示三向相互作用(疼痛×疲劳/虚弱×睡眠问题)具有统计学意义。在后续分析中,疼痛和抑郁情绪之间的关系在两个部分范围内被加重(放大):1)当完全控制睡眠问题(例如,睡眠障碍的一部分)时,可以完全控制疲劳/虚弱。疼痛-疲劳/虚弱症状对),以及2)如果无法控制睡眠问题(例如,疼痛-疲劳/虚弱-睡眠问题症状群的一部分),则无法控制疲劳/虚弱。否则,随着对疲劳/虚弱或睡眠问题的控制减弱,该关系减弱(缓冲)。结论:通过减少标准误差,SRC消除了包括症状对和症状簇的三向相互作用。疲劳/弱者的调节变量的中低水平会放大疼痛和抑郁情绪之间的关系。但是,当针对睡眠问题的共主持人变量伴随疲劳/虚弱时,只有两种症状的频繁或无情水平都会放大这种关系。这些发现表明,在先前的一项随机试验中,涉及抑郁情绪的抵消机制可以解释认知行为干预降低疼痛,疲劳和睡眠障碍的严重程度的有效性。

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