Sereika Susan M, Zheng Yaguang, Hu Lu, Burke Lora E
1 University of Pittsburgh, Pittsburgh, PA, USA.
2 Boston College, Chestnut Hill, MA, USA.
West J Nurs Res. 2017 Aug;39(8):1028-1044. doi: 10.1177/0193945917697221. Epub 2017 Apr 24.
Persons receiving treatment for weight loss often demonstrate heterogeneity in lifestyle behaviors and health outcomes over time. Traditional repeated measures approaches focus on the estimation and testing of an average temporal pattern, ignoring the interindividual variability about the trajectory. An alternate person-centered approach, group-based trajectory modeling, can be used to identify distinct latent classes of individuals following similar trajectories of behavior or outcome change as a function of age or time and can be expanded to include time-invariant and time-dependent covariates and outcomes. Another latent class method, growth mixture modeling, builds on group-based trajectory modeling to investigate heterogeneity within the distinct trajectory classes. In this applied methodologic study, group-based trajectory modeling for analyzing changes in behaviors or outcomes is described and contrasted with growth mixture modeling. An illustration of group-based trajectory modeling is provided using calorie intake data from a single-group, single-center prospective study for weight loss in adults who are either overweight or obese.
接受减肥治疗的人在生活方式行为和健康结果方面往往会随着时间表现出异质性。传统的重复测量方法侧重于对平均时间模式的估计和检验,而忽略了轨迹的个体间变异性。另一种以个体为中心的方法,即基于群体的轨迹建模,可用于识别遵循相似行为轨迹或作为年龄或时间函数的结果变化轨迹的不同潜在个体类别,并且可以扩展到包括时间不变和时间依赖的协变量及结果。另一种潜在类别方法,即生长混合建模,建立在基于群体的轨迹建模基础上,以研究不同轨迹类别内的异质性。在这项应用方法学研究中,描述了用于分析行为或结果变化的基于群体的轨迹建模,并将其与生长混合建模进行对比。使用来自一项针对超重或肥胖成年人的单组、单中心减肥前瞻性研究的卡路里摄入数据,给出了基于群体的轨迹建模示例。