Group-based multi-trajectory modeling

D.S. Nagin, B.L. Jones, Valéria Lima Passos, R.E. Tremblay

Research output: Contribution to journalArticleAcademicpeer-review

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 CO2 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.

Original languageEnglish
Pages (from-to)2015-2023
Number of pages9
JournalStatistical Methods in Medical Research
Volume27
Issue number7
DOIs
Publication statusPublished - Jul 2018

Keywords

  • Longitudinal analysis of multiple outcomes
  • group-based trajectory modeling
  • latent class analysis
  • trajectories of multiple disease biomarkers
  • MULTIVARIATE

Cite this

Nagin, D.S. ; Jones, B.L. ; Lima Passos, Valéria ; Tremblay, R.E. / Group-based multi-trajectory modeling. In: Statistical Methods in Medical Research. 2018 ; Vol. 27, No. 7. pp. 2015-2023.
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Group-based multi-trajectory modeling. / Nagin, D.S.; Jones, B.L.; Lima Passos, Valéria; Tremblay, R.E.

In: Statistical Methods in Medical Research, Vol. 27, No. 7, 07.2018, p. 2015-2023.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Group-based multi-trajectory modeling

AU - Nagin, D.S.

AU - Jones, B.L.

AU - Lima Passos, Valéria

AU - Tremblay, R.E.

PY - 2018/7

Y1 - 2018/7

N2 - 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 CO2 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.

AB - 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 CO2 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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KW - group-based trajectory modeling

KW - latent class analysis

KW - trajectories of multiple disease biomarkers

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