Framework to construct and interpret latent class trajectory modelling

Lennon, Hannah, Kelly, Scott, Sperrin, Matthew, Buchan, Iain ORCID: 0000-0003-3392-1650, Cross, Amanda J, Leitzmann, Michael, Cook, Michael B and Renehan, Andrew G
(2018) Framework to construct and interpret latent class trajectory modelling. BMJ Open, 8 (7). e020683-e020683.

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<h4>Objectives</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>We propose a framework to construct and select a 'core' LCTM, which will facilitate generalisability of results in future studies.

Item Type: Article
Uncontrolled Keywords: latent class models, growth curves, growth mixture models, lifetime obesity, trajectories
Depositing User: Symplectic Admin
Date Deposited: 14 May 2019 10:37
Last Modified: 19 Jan 2023 00:45
DOI: 10.1136/bmjopen-2017-020683
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