Lennon, Hannah and Kelly, Scott and Sperrin, Matthew and Buchan, Iain and Cross, Amanda J. and Leitzmann, Michael and Cook, Michael B. and Renehan, Andrew G. (2018) Framework to construct and interpret latent class trajectory modelling. BMJ OPEN, 8 (7): e020683. ISSN 2044-6055,
Full text not available from this repository. (Request a copy)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 288993 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 structureconcordance between models F and G were moderate (Cohen : 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | BODY-MASS INDEX; NATIONAL INSTITUTES; CANCER-RISK; HEALTH; COHORT; AARP; latent class models; growth curves; growth mixture models; lifetime obesity; trajectories |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Institut für Epidemiologie und Präventivmedizin |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 08 Jan 2020 15:00 |
| Last Modified: | 08 Jan 2020 15:00 |
| URI: | https://pred.uni-regensburg.de/id/eprint/13899 |
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