The reporting and handling of missing data in longitudinal studies of older adults is suboptimal: a methodological survey of geriatric journals
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: Missing data are common in longitudinal studies, and more so, in studies of older adults, who are susceptible to health and functional decline that limit completion of assessments. We assessed the extent, current reporting, and handling of missing data in longitudinal studies of older adults. METHODS: Medline and Embase databases were searched from 2015 to 2019 for publications on longitudinal observational studies conducted among persons ≥55 years old. The search was restricted to 10 general geriatric journals published in English. Reporting and handling of missing data were assessed using questions developed from the recommended standards. Data were summarised descriptively as frequencies and proportions. RESULTS: A total of 165 studies were included in the review from 7032 identified records. In approximately half of the studies 97 (62.5%), there was either no comment on missing data or unclear descriptions. The percentage of missing data varied from 0.1 to 55%, with a 14% average among the studies that reported having missing data. Complete case analysis was the most common method for handling missing data with nearly 75% of the studies (n = 52) excluding individual observations due to missing data, at the initial phase of study inclusion or at the analysis stage. Of the 10 studies where multiple imputation was used, only 1 (10.0%) study followed the guideline for reporting the procedure fully using online supplementary documents. CONCLUSION: The current reporting and handling of missing data in longitudinal observational studies of older adults are inadequate. Journal endorsement and implementation of guidelines may potentially improve the quality of missing data reporting. Further, authors should be encouraged to use online supplementary files to provide additional details on how missing data were addressed, to allow for more transparency and comprehensive appraisal of studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.897 | 0.978 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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 it