Modeling developmental trajectories with nonrandomly missing data: investigating trajectories of frailty using data from the Manitoba Follow-up Study
Bibliographic record
Abstract
Frailty is an age-related syndrome, marked by declines in various organ systems. Its baseline and progress are linked with increased mortality. Notably, frailty trajectories exhibit significant individual variability. Understanding these trajectories and their associated factors is vital. The study assesses conventional and extended Group-based trajectory analysis (GBTA) in mapping frailty trajectories, especially considering different missing data mechanisms. Objectives: Compare conventional and extended GBTA under various conditions. Explore frailty trajectories in older men using the Manitoba Follow-up Study (MFUS) data. Methods: Simulation studies were conducted to contrast the GBTA models. Data were crafted from distinct trajectory scenarios and missing data mechanisms. Metrics like absolute error and mean squared error gauged the models' accuracy. MFUS data, a Canadian longitudinal study on ageing, was employed for real-world frailty trajectory examination. A frailty index using daily living activities and comorbidities was established, measuring health variation. Results: Simulations showed the extended GBTA was superior when latent classes weren’t initially distinct, under varied missing data scenarios. With the MFUS data, the extended GBTA identified four frailty trajectories. Participants' age correlated with their frailty trajectory, but marital status and living arrangements didn’t. Conclusion: The extended GBTA outperforms the conventional method, especially with latent classes not distinctly separated initially. By leveraging both GBTAs on MFUS data, we gain insight into frailty's growth heterogeneity. This aids in devising impactful future prevention initiatives by understanding the nuances of frailty trajectories.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".