Identifying Symptom Clusters in People with Cancer

描述（由申请人提供）：症状和副作用，如疲劳;脱发，口腔疼痛，疼痛和睡眠问题是癌症治疗期间和之后的重要问题。大多数关于症状的研究都嵌入在单一症状框架中，但癌症治疗经验的现实是，症状是以群体或群体的形式出现的。用于指导研究的范例与癌症患者的“真实的世界”经验之间的这种差距是建立癌症护理中症状管理知识库的主要限制。该项目的目的是通过识别癌症治疗期间经历的症状群来解决这一差距，确定特定的人口统计学和临床变量是否预测症状群成员资格，并检查症状群成员资格与情绪和身体功能的关系。我们将使用之前四项研究中收集的数据，其中包括有关症状，情绪和功能的信息，但并不是所有人都使用相同的方法来获取这些信息。这些研究有些不同的事实是这个项目的优势，因为我们将为每项研究分别进行统计分析，并将能够查看聚类，看看它们是否基于治疗类型（放射治疗或化疗）看起来相似，以确认或交叉验证结果。我们将使用诸如年龄、疾病分期和合并症等变量来探索症状群成员的预测因素。这些信息是有帮助的，因为它最终可能告诉我们谁是经历许多严重症状的最高风险。分层聚类分析将用于识别和交叉验证每种癌症治疗类型和癌症诊断（乳腺癌或肺癌）的症状群。在建立聚类成员关系后，我们将使用分层线性模型来研究聚类成员关系与情绪和功能的关系。这个项目是创新的，因为它解决了一个未研究的问题，并将提供必要的信息，其他调查人员谁是移动到多个症状模型。该项目的结果将提供有关一种方法的重要信息，该方法被认为对症状群的研究有用，但除了开发评估工具外，从未应用于癌症症状的已发表工作。因为我们在两种类型的癌症治疗中进行分析，并在每种类型的乳腺癌治疗和两种类型的肺癌治疗中进行交叉验证，分析的结果将提供关于当比较两种不同的治疗方法时，症状群的表现如何的信息，并可能挑战我们传统的临床假设，即人们经历的症状群是治疗的类型。

DESCRIPTION (provided by applicant): Symptoms and side effects like fatigue; hair loss, sore mouth, pain, and problems sleeping are important issues during and following cancer treatment. Most research on symptoms is embedded in a single symptom framework but the reality of the experience of cancer treatment is that symptoms occur in groups or dusters. This gap between the paradigm used to guide research and the "real world" experience of people with cancer is a major limitation in building the knowledge base for symptom management in cancer care. The purpose of this project is to address this gap by identifying symptom clusters experienced during cancer treatment, determining if specific demographic and clinic variables predict symptom cluster membership, and examining the relationship of symptom cluster membership to mood and physical function over time. We will use data collected in four prior studies that included information about symptoms, mood, and function but did not all use the same approach for getting this information. The fact that the studies are somewhat different is a strength for this project because we will be doing the statistical analysis separately for each study and will be able to look at the clusters to see if they look similar based upon type of treatment (radiation treatment or chemotherapy) to confirm or cross-validate the findings. We will explore predictors of symptom cluster membership using variables such as age, stage of disease, and comorbidity. This information is helpful because it may eventually tell us who is at highest risk for experiencing numerous severe symptoms. Hierarchical cluster analysis will be used to identify and cross-validate the symptom clusters in each type of cancer treatment and by cancer diagnosis (breast or lung cancer). After cluster membership is established, we will use hierarchical linear modeling to examine the relationship of cluster membership to mood and function. This project is innovative because it addresses an unstudied problem and will provide information essential to other investigators who are moving to multiple symptom models. The results of this project will provide important information about an approach that is assumed to be useful in pursuing work on symptom clusters but has never been applied in published work on cancer symptoms except in developing assessment tools. Because we are doing our analyses within two types of cancer treatment and cross-validating within each type of treatment for breast cancer and across two types of treatment for lung cancer, the results of the analyses will provide information about how well the symptom clusters hold up when the two different treatments are compared and may challenge our traditional clinical assumption that the clusters of symptoms people experience are a result of type of treatment.