Cited by 420Statistical Methods in EpidemiologyPsychometric Methodologies and TestingStatistical Methods and Bayesian Inference

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Framework to construct and interpret latent class trajectory modelling

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Lennon, Hannah, Kelly, Scott, Sperrin, Matthew, Buchan, Iain, Cross, Amanda J, Leitzmann, Michael, Cook, Michael B, Renehan, Andrew G·2018·BMJ Open

DOI 10.1136/bmjopen-2017-020683PMC PMC6042544

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Abstract

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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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: Lennon, Hannah
Kelly, Scott
Sperrin, Matthew
Buchan, Iain
Cross, Amanda J
Leitzmann, Michael
Cook, Michael B
Renehan, Andrew G
Venue: BMJ Open
Publisher: BMJ
Published: 2018-07
Volume: 8
Issue: 7
Pages: e020683
ISSN: 2044-6055 (ISSN-L)
DOI: 10.1136/bmjopen-2017-020683
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

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