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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.18108 |
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| _version_ | 1866913906900860928 |
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| author | Hsiao, Yi-Chen Chen, Chun-Yuan Tang, Mei-Fen |
| author_facet | Hsiao, Yi-Chen Chen, Chun-Yuan Tang, Mei-Fen |
| contents | Group-based trajectory modeling (GBTM) is commonly used to identify longitudinal patterns in health outcomes among older adults, with determining the optimal number of groups being a crucial step. While statistically grounded criteria are primarily relied upon, clinical relevance is gradually emphasized in medicine to ensure that the identified trajectory heterogeneity appropriately reflects changes in a disease or symptom over time. However, such considerations are often judged through visual comparisons, without concrete approaches for their application. To address this, the Area Between Trajectories (ABTs) was introduced as insights for quantifying trajectory group differences. Using a simulated sleep quality dataset, GBTM was applied to build and compare models. Subsequently, ABTs was demonstrated to show how it works, while also highlighting its limitations and potential applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18108 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Area between trajectories: Insights into optimal group selection and trajectory heterogeneity in group-based trajectory modeling Hsiao, Yi-Chen Chen, Chun-Yuan Tang, Mei-Fen Applications Group-based trajectory modeling (GBTM) is commonly used to identify longitudinal patterns in health outcomes among older adults, with determining the optimal number of groups being a crucial step. While statistically grounded criteria are primarily relied upon, clinical relevance is gradually emphasized in medicine to ensure that the identified trajectory heterogeneity appropriately reflects changes in a disease or symptom over time. However, such considerations are often judged through visual comparisons, without concrete approaches for their application. To address this, the Area Between Trajectories (ABTs) was introduced as insights for quantifying trajectory group differences. Using a simulated sleep quality dataset, GBTM was applied to build and compare models. Subsequently, ABTs was demonstrated to show how it works, while also highlighting its limitations and potential applications. |
| title | Area between trajectories: Insights into optimal group selection and trajectory heterogeneity in group-based trajectory modeling |
| topic | Applications |
| url | https://arxiv.org/abs/2506.18108 |