MISSING DATA IN LONGITUDINAL STUDIES OF AGING: THE GOOD, THE BAD, AND THE UGLY
Bibliographic record
Abstract
In longitudinal studies of aging, missing data are inevitable. In the literature on the association between grip strength and survival time in older persons, the methods used to deal with missing values are generally not even mentioned. The default in standard statistical software is listwise deletion - removing all observations with any missing data. Missing data have been classified into three types: missing completely at random (the good), missing at random (the bad) and missing not at random (the ugly). Depending on the type of missing data, listwise deletion may produce biased results.The objectives of this presentation are: 1) To identify different types of missing data that may arise within a longitudinal study of aging and 2) To assess the estimates of the association between grip strength and mortality that result from applying different methods for handling missing data.Using data from the Cardiovascular Health Study, a longitudinal study of 5,201 older persons, we: 1) examined the types of missing data; 2) investigated the association between grip strength and survival time, applying three common methods of handling missing data: listwise deletion, last value carried forward (LOCF), and multiple imputation (MI). We identified all three types of missing data in the CHS. Compared to the method of listwise deletion, estimates of the association between grip strength and survival were weaker when applying LOCF, and stronger with MI. Methods of handling missing data in longitudinal studies of aging should be considered carefully in order to limit the potential for biased results.
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How this classification was reachedexpand
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".