Snapshot of Statistical Methods Used in Geriatric Cohort Studies: How Do We Treat Missing Data in Publications?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Geriatric studies often miss data of frail participants. The aim of this paper is to explore which missing data methodologies have entered current practice and to discuss the potential impact of ignoring the issue. Methods: A Sample of 103 articles was drawn from key cohort studies: Health ABC, InCHIANTI, LASA, BLSA, EPESE, and KLoSHA. The studies were classified according to missing data methodologies used. Results: Seventy-seven percent described the selected analysis data set and only 28% used a method of handling all available observations per case. Missing data dedicated methods were rare (< 10%), applying single or multiple imputations for baseline variables. Studies with longer follow-up periods more often employed longitudinal analysis methodologies. Conclusions: Despite the recognition that missing data is a major problem in studies of older persons, few published studies account for missing data using limited methodologies; this could affect the validity of study conclusions. We propose researchers apply Joint Modeling of longitudinal and time-to-event data, using shared-parameter model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.221 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it