%0 Journal Article
%T Framework to construct and interpret latent class trajectory modelling
%A Amanda J Cross
%A Andrew G Renehan
%A Hannah Lennon
%A Iain Buchan
%A Matthew Sperrin
%A Michael B Cook
%A Michael Leitzmann
%A Scott Kelly
%J -
%D 2018
%R 10.1136/bmjopen-2017-020683
%X Objectives 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.

Methods 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.

Results 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¡ªconcordance between models F and G were moderate (Cohen ¦Ê: 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.

Conclusion We propose a framework to construct and select a ¡®core¡¯ LCTM, which will facilitate generalisability of results in future studies
%U https://bmjopen.bmj.com/content/8/7/e020683