Lennon Hannah, Kelly Scott, Sperrin Matthew, Buchan Iain, Cross Amanda J, Leitzmann Michael, Cook Michael B, Renehan Andrew G
Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
MRC Health eResearch Centre (HeRC), Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK.
BMJ Open. 2018 Jul 7;8(7):e020683. doi: 10.1136/bmjopen-2017-020683.
Latent class trajectory modelling (LCTM) is a relatively new methodology in epidemiology to describe life-course exposures, which simplifies heterogeneous populations into homogeneous patterns or classes. However, for a given dataset, it is possible to derive scores of different models based on number of classes, model structure and trajectory property. Here, we rationalise a systematic framework to derive a 'core' favoured model.
We developed an eight-step framework: step 1: a scoping model; step 2: refining the number of classes; step 3: refining model structure (from fixed-effects through to a flexible random-effect specification); step 4: model adequacy assessment; step 5: graphical presentations; step 6: use of additional discrimination tools ('degree of separation'; Elsensohn's envelope of residual plots); step 7: clinical characterisation and plausibility; and step 8: sensitivity analysis. We illustrated these steps using data from the NIH-AARP cohort of repeated determinations of body mass index (BMI) at baseline (mean age: 62.5 years), and BMI derived by weight recall at ages 18, 35 and 50 years.
From 288 993 participants, we derived a five-class model for each gender (men: 177 455; women: 111 538). From seven model structures, the favoured model was a proportional random quadratic structure (model F). Favourable properties were also noted for the unrestricted random quadratic structure (model G). However, class proportions varied considerably by model structure-concordance between models F and G were moderate (Cohen κ: men, 0.57; women, 0.65) but poor with other models. Model adequacy assessments, evaluations using discrimination tools, clinical plausibility and sensitivity analyses supported our model selection.
We propose a framework to construct and select a 'core' LCTM, which will facilitate generalisability of results in future studies.
潜在类别轨迹建模（LCTM）是流行病学中一种相对较新的方法，用于描述生命历程暴露情况，它将异质人群简化为同质模式或类别。然而，对于给定的数据集，基于类别数量、模型结构和轨迹属性，可以推导出不同模型的得分。在此，我们提出一个系统框架来推导一个“核心”优选模型。
我们开发了一个八步框架：第一步：范围界定模型；第二步：细化类别数量；第三步：细化模型结构（从固定效应到灵活的随机效应规范）；第四步：模型充分性评估；第五步：图形展示；第六步：使用额外的判别工具（“分离度”；埃尔森索恩残差图包络）；第七步：临床特征描述和合理性；第八步：敏感性分析。我们使用美国国立卫生研究院-美国退休人员协会队列的数据说明了这些步骤，该队列在基线时重复测定体重指数（BMI）（平均年龄：62.5岁），并通过回忆18岁、35岁和50岁时的体重得出BMI。
从288993名参与者中，我们为每个性别推导了一个五类模型（男性：177455名；女性：111538名）。从七种模型结构中，优选模型是比例随机二次结构（模型F）。无限制随机二次结构（模型G）也具有良好的属性。然而，类别比例因模型结构而有很大差异——模型F和G之间的一致性适中（科恩κ系数：男性为0.57；女性为0.65），但与其他模型的一致性较差。模型充分性评估、使用判别工具的评估、临床合理性和敏感性分析支持了我们的模型选择。
我们提出了一个构建和选择“核心”LCTM的框架，这将有助于未来研究结果的可推广性。
