Frequency of data collection and estimation of trajectories of physical functioning and their associations with survival in older men: analyses of longitudinal data from the Manitoba Follow-Up Study
Bibliographic record
Abstract
OBJECTIVE: In studies of trajectories of physical functioning among older people, the data cannot be measured continuously, but only at certain time points in prespecified cycles. We examine how data collection cycles can affect the estimation of trajectories and their associations with survival. STUDY DESIGN AND SETTING: Longitudinal data from the Manitoba Follow-Up Study (MFUS), with 12 measurements collected annually from 2004 to 2015, are analysed using a summary measures of physical functioning from the Short Form-36 questionnaire. Based on the joint models of the functioning trajectories and risk of death, we compare the estimations among models using different frequency of data collection (annually, biennially and triennially). RESULTS: Our 2004 baseline includes 964 men who were survivors from the original MFUS cohort with mean age of 84 years and range between 75 and 94 years. Results from analysis of annual data indicate that the mean physical functioning is significantly decreasing over time. Further, the rate of decline is increasing over time. The current value of physical functioning is significantly associated with the hazard of death (p<0.001), whereas the association between the change rate and mortality is marginally significant (p<0.10). Results from analysis of biennial and triennial data reveal similar trajectory patterns of physical functioning, but could not reveal the association between the change rate of physical functioning and mortality. The frequency of data collection also impacts substantially on the estimation of heterogeneity of functioning trajectory. The prediction of mortality risk obtained using annual measurements of physical functioning are better than using biennial or triennial measurements, while the predictions obtained using biennial or triennial measurements are almost equivalent. CONCLUSION: The impact of frequency of data collection depends on the shape of functional trajectories and its linking structure to survival outcome.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".