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Framework to construct and interpret latent class trajectory modelling
Open accessAbstract
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.
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“The best-fitting model was identified using the Akaike information criterion, sample adjusted Bayesian information criterion, entropy index, Vuong–Lo–Mendell–Rubin test, and clinical utility of the model [32].”
From Predictors of chronic loneliness during adolescence: a population-based cohort study (2022) · cites this paper as [32]
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“Step 7 We assessed for clinical characterisation and plausibility using four approaches: (1) assessing the clinical meaningfulness of the trajectory patterns, aiming to include classes with at least 1% capture of the population; (2) assessing the clinical plausibility of the trajectory classes; (3) tabulation of chara…”
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Metadata
- Authors
- Lennon, H., Kelly, S., Sperrin, M., Buchan, I., Cross, A. J., Leitzmann, M., Cook, M. B., & Renehan, A. G.
- Affiliations
- University of Manchester (GB)
- National Institutes of Health (US)
- Imperial College London (GB)
- University of Regensburg (DE)
- Corresponding author
- Lennon, Hannah
- Renehan, Andrew G
- ORCID
- Venue
- BMJ Open
- Publisher
- BMJ
- Published
- 2018-07
- Volume
- 8
- Issue
- 7
- Pages
- e020683
- ISSN
- 2044-6055 (ISSN-L)
- PMC
- PMC6042544
- OpenAlex
- W2794722104
- Cited by
- 419
- References
- 34
- Citations by year
- 2026: 73 · 2025: 107 · 2024: 86 · 2023: 47 · 2022: 44 · 2021: 36 · 2020: 15 · 2019: 10
- FWCI
- 12.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMJ Open (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Regensburg Publication Server (University of Regensburg) (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Cancer Research UK
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- life course exposure, model selection, number of classes, model adequacy assessment, degree of separation, residual plots, body mass index trajectories, sensitivity analysis
- MeSH terms
- Latent Class Analysis, Adolescent, Adult, Body Weight, Diet, Female, Humans, Life Style, Male, Middle Aged, Surveys and Questionnaires, Mental Recall, Risk Factors, United States, Cohort Studies, Body Mass Index, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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