The Case of the Missing Data: Methods of Dealing with Dropouts and other Research Vagaries
Bibliographic record
Abstract
Missing data are common in most studies, especially when subjects are followed over time. This can jeopardize the validity of a study because of reduced power to detect differences, and especially because subjects who are lost to follow-up rarely represent the group as a whole. There are several approaches to handling missing data, but some may result in biased estimates of the treatment effect, and others may overestimate the significance of the statistical tests. When cross-sectional data (for example, demographic and background information and a single outcome measurement time) are missing, replacement with the group mean leads to an underestimate of the standard deviation (SD) and inflation of the Type I error rate. Using regression estimates, especially with error built into the imputed value, lessens but does not eliminate this problem. Multiple imputation preserves the estimates of both the mean and the SD, even when a significant proportion of the data are missing. With longitudinal studies, the last observation carried forward (LOCF) approach preserves the sample size, but may make unwarranted assumptions about the missing data, resulting in either underestimating or overestimating the treatment effects. Growth curve analysis makes maximal use of the existing data and makes fewer assumptions.
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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 | 0.403 | 0.631 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".