The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Group-based multi-trajectory modeling
Abstract
Identifying and monitoring multiple disease biomarkers and other clinically important factors affecting the course of a disease, behavior or health status is of great clinical relevance. Yet conventional statistical practice generally falls far short of taking full advantage of the information available in multivariate longitudinal data for tracking the course of the outcome of interest. We demonstrate a method called multi-trajectory modeling that is designed to overcome this limitation. The method is a generalization of group-based trajectory modeling. Group-based trajectory modeling is designed to identify clusters of individuals who are following similar trajectories of a single indicator of interest such as post-operative fever or body mass index. Multi-trajectory modeling identifies latent clusters of individuals following similar trajectories across multiple indicators of an outcome of interest (e.g., the health status of chronic kidney disease patients as measured by their eGFR, hemoglobin, blood CO 2 levels). Multi-trajectory modeling is an application of finite mixture modeling. We lay out the underlying likelihood function of the multi-trajectory model and demonstrate its use with two examples.
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Self-citationAdvances in Group-Based Trajectory Modeling and an SAS Procedure for Estimating Them
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Rod, N. H., Bengtsson, J., Budtz-Jørgensen, E., Clipet-Jensen, C., Taylor-Robinson, D., Andersen, A.-M. N., Dich, N., & Rieckmann, A. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30621-8 · Cited by 232
Orri, M., Galera, C., Turecki, G., Forte, A., Renaud, J., Boivin, M., Tremblay, R. E., Côté, S. M., & Geoffroy, M.-C. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.0174 · Cited by 141
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Hébert, J. J., Abraham, E., Wedderkopp, N., Bigney, E., Richardson, E., Darling, M., Hall, H., Fisher, C. G., Rampersaud, Y. R., Thomas, K. C., et al. (2020) · Spine · doi:10.1097/brs.0000000000003587 · Cited by 74
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“Definition of Trajectory Groups Multivariable latent class models using Group-Based Trajectory Modeling (GBTM) assessed longitudinal variations in repeated measures of fatigue.(11,12,20-22) The procedure is extensively presented in the Data Supplement.”
MethodResult
Metadata
- Authors
- Nagin, D. S., Jones, B. L., Passos, V. L., & Tremblay, R. E.
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2016-10-17
- Volume
- 27
- Issue
- 7
- Pages
- 2015-2023
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- OpenAlex
- W2537229494
- Cited by
- 932
- References
- 13
- FWCI
- 17.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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