Cargando…
Framework to construct and interpret latent class trajectory modelling
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 b...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042544/ https://www.ncbi.nlm.nih.gov/pubmed/29982203 http://dx.doi.org/10.1136/bmjopen-2017-020683 |
_version_ | 1783339175432421376 |
---|---|
author | Lennon, Hannah Kelly, Scott Sperrin, Matthew Buchan, Iain Cross, Amanda J Leitzmann, Michael Cook, Michael B Renehan, Andrew G |
author_facet | Lennon, Hannah Kelly, Scott Sperrin, Matthew Buchan, Iain Cross, Amanda J Leitzmann, Michael Cook, Michael B Renehan, Andrew G |
author_sort | Lennon, Hannah |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6042544 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-60425442018-07-16 Framework to construct and interpret latent class trajectory modelling Lennon, Hannah Kelly, Scott Sperrin, Matthew Buchan, Iain Cross, Amanda J Leitzmann, Michael Cook, Michael B Renehan, Andrew G BMJ Open Research Methods 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. BMJ Publishing Group 2018-07-07 /pmc/articles/PMC6042544/ /pubmed/29982203 http://dx.doi.org/10.1136/bmjopen-2017-020683 Text en © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an open access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Research Methods Lennon, Hannah Kelly, Scott Sperrin, Matthew Buchan, Iain Cross, Amanda J Leitzmann, Michael Cook, Michael B Renehan, Andrew G Framework to construct and interpret latent class trajectory modelling |
title | Framework to construct and interpret latent class trajectory modelling |
title_full | Framework to construct and interpret latent class trajectory modelling |
title_fullStr | Framework to construct and interpret latent class trajectory modelling |
title_full_unstemmed | Framework to construct and interpret latent class trajectory modelling |
title_short | Framework to construct and interpret latent class trajectory modelling |
title_sort | framework to construct and interpret latent class trajectory modelling |
topic | Research Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042544/ https://www.ncbi.nlm.nih.gov/pubmed/29982203 http://dx.doi.org/10.1136/bmjopen-2017-020683 |
work_keys_str_mv | AT lennonhannah frameworktoconstructandinterpretlatentclasstrajectorymodelling AT kellyscott frameworktoconstructandinterpretlatentclasstrajectorymodelling AT sperrinmatthew frameworktoconstructandinterpretlatentclasstrajectorymodelling AT buchaniain frameworktoconstructandinterpretlatentclasstrajectorymodelling AT crossamandaj frameworktoconstructandinterpretlatentclasstrajectorymodelling AT leitzmannmichael frameworktoconstructandinterpretlatentclasstrajectorymodelling AT cookmichaelb frameworktoconstructandinterpretlatentclasstrajectorymodelling AT renehanandrewg frameworktoconstructandinterpretlatentclasstrajectorymodelling |