Evidence map›Paper›PMID 29982203›Full record

ArticleBMJ open2018

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 G Renehan

Abstract read
In one paragraph

Article in BMJ open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 227 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
227citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

227 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  8. Built Environment and Loneliness Trajectories Among Middle-Aged and Older Adults in China.Journal of urban health : bulletin of the New York Academy of Medicine · 2026
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167 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Hannah LennonDivision of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.ORCID 0000-0002-4925-8091
Scott KellyDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
Matthew SperrinMRC Health eResearch Centre (HeRC), Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK.
Iain BuchanMRC Health eResearch Centre (HeRC), Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK.
Amanda J CrossDepartment of Epidemiology and Biostatistics, Imperial College, London, UK.
Michael LeitzmannDepartment of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany.
Michael B CookDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
Andrew G RenehanDivision of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

Funding

Cancer Research UK 17962Cancer Research UK 25004Medical Research Council MC_PC_13042
6 · The paper itself

Abstract

objectivesLatent 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.

methodsWe 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.

resultsFrom 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.

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

Indexed as

Body Mass IndexBody WeightLatent Class AnalysisAdolescentAdultCohort StudiesDietFemaleHumansLife StyleMaleMental RecallMiddle AgedRisk FactorsSurveys and QuestionnairesUnited Statesgrowth curvesgrowth mixture modelslatent class modelslifetime obesitytrajectories

Identifiers

PMID29982203
PMCPMC6042544

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.