Nell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road NE, Room 225, Atlanta, GA, 30322-4207.
Res Nurs Health. 2014 Feb;37(1):65-74. doi: 10.1002/nur.21572.
In this article, we address statistical techniques appropriate for examining longitudinal changes in cancer symptom clusters. When the cluster structure is not pre-determined, researchers may examine symptom clusters either at each time point or use composite scores to examine the symptom clusters across time points. When the cluster structures are pre-determined, the statistical techniques depend on the research assumptions or purposes. Multilevel modeling, generalized estimating equations, latent growth curve modeling, and multivariate repeated-measure analysis of variance are good choices for exploring whole cluster changes over time. Alternately, confirmatory factor analysis and path analysis are appropriate techniques for examining changes in symptom relationships within clusters over time. Each technique is described, with examples and strengths and weaknesses.
在本文中，我们讨论了适用于检查癌症症状群纵向变化的统计技术。当聚类结构未预先确定时，研究人员可以在每个时间点检查症状群，也可以使用综合评分来检查跨时间点的症状群。当聚类结构预先确定时，统计技术取决于研究假设或目的。多层次建模、广义估计方程、潜在增长曲线建模和多元重复测量方差分析是探索整个聚类随时间变化的较好选择。或者，验证性因子分析和路径分析是用于检查聚类内症状关系随时间变化的合适技术。每种技术都有描述，并附有示例以及优缺点。
