Framework to construct and interpret latent class trajectory modelling

Hannah Lennon, Scott Kelly, Matthew Sperrin, Iain Buchan, Amanda J. Cross, Michael Leitzmann, Michael B. Cook, Andrew Renehan

Research output: Contribution to journalArticlepeer-review


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 - 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. Conclusion We propose a framework to construct and select a 'core' LCTM, which will facilitate generalisability of results in future studies.

Original languageEnglish
Article numbere020683
Number of pages10
JournalBMJ Open
Issue number7
Early online date7 Jul 2018
Publication statusPublished - 2018


  • Latent class models
  • Growth curves
  • growth mixture models
  • body mass index
  • Trajectories
  • lifetime obesity

Research Beacons, Institutes and Platforms

  • Manchester Cancer Research Centre


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