Study of quality-of-life trajectories at late life: impact of statistical models and study design
Bibliographic record
Abstract
The importance of understanding the developmental pathway of physical functioning for old population is well-recognized. To accurately estimate the true trajectory of physical functioning, a well-designed longitudinal study is needed. The relationship between physical functioning trajectories and survival are not well-understood, which requires more advanced statistical approaches. The purpose of this study is to investigate the impact of statistical approaches and study design in a longitudinal study on health and aging. Growth curve models were first conducted only using a sample of 200 living males from the Manitoba Follow Up Study (MFUS) to identify the shape of physical functioning trajectory. Then using all available sample over 12 years from MFUS, we compared three statistical approaches: 1) the growth curve model; 2) the extended Cox model; and 3) the joint model for longitudinal and survival data. We investigated the impact of study design on parameter estimations of physical functioning trajectory and survival function. The overall trend of physical functioning was decreasing at an accelerating rate over year. According to the best joint model, those with worse physical functioning or higher declining rate would have higher risk of death. Joint model approach provides better performance in aging studies with an interest on the association between longitudinal markers and the risk of death. The impact of study design on parameter estimations for describing the longitudinal trajectory is minimal as soon as we have enough data points to estimate the individual shape of trajectory (e.g., three points for linear growth and four points for quadratic growth) and large sample size. The influence of data collection cycle on the association of current longitudinal marker and risk of death is relatively small. The influence of data collection cycle on the association of derivative of longitudinal markers and the risk of death in the survival submodel is large.
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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 | 0.505 | 0.589 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".