Using classification and regression trees to model missingness in youth BMI, height and body mass data
Bibliographic record
Abstract
INTRODUCTION: Research suggests that there is often a high degree of missingness in youth body mass index (BMI) data derived from self-reported measures, which may have a large effect on research findings. The first step in handling missing data is to examine the levels and patterns of missingness. However, previous studies examining youth BMI missingness used logistic regression, which is limited in its ability to discern subgroups or identify a hierarchy of importance for variables, aspects that may go a long way in helping understand missing data patterns. METHODS: This study used sex-stratified classification and regression tree (CART) models to examine missingness in height, body mass and BMI data among 74 501 youth participating in the 2018/19 COMPASS study (a prospective cohort study examining health behaviours among Canadian youth), where 31% of BMI data were missing. Diet, movement, academic, mental health and substance use variables were examined for associations with missingness in height, body mass and BMI. RESULTS: CART models indicated that the combination of being younger, having a selfperception of being overweight, being less physically active and having poorer mental health yielded female and male subgroups highly likely to be missing BMI values. Survey respondents who did not perceive themselves as overweight and who were older were unlikely to be missing BMI values. CONCLUSION: The subgroups identified by the CART models indicate that a sample that deletes cases with missing BMI would be biased towards physically, emotionally and mentally healthier youth. Given the ability of CART models to identify these subgroups and a hierarchy of variable importance, they are an invaluable tool for examining missing data patterns and appropriate handling of missing data.
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.
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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".