Assessing the impact of missing data in youth overweight and obesity research: complete case analysis versus multiple imputation
Bibliographic record
Abstract
Youth overweight and obesity (OWOB) surveillance often uses body mass index (BMI) derived from self-reported height and weight, but these measures can suffer from high proportions of missing data. Complete case analysis (CCA) is the most common approach to handle missing data, but this approach can introduce bias if missing data are not missing completely at random. Using BMI and related covariate data from 36,546 female and 37,126 male youth aged 12–19 years who participated in the COMPASS study in 2018/19, where approximately 30% of BMI data were missing, results and inference were compared between CCA and multiple imputation (MI) approaches to examine associations with youth BMI. Results of regression joint models showed contrasting findings between MI and CCA, highlighting that appropriate methodological choices in the handling of missing data are essential in youth OWOB research and that choices can impact research inference and thereby associated policy and programming recommendations.
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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 | 0.535 | 0.716 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| 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 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".