742Analyzing the statistical pitfalls of treating mediators as confounders
Bibliographic record
Abstract
Abstract Background Numerous studies indicated that infants born small-for-gestational-age (SGA) are at higher risk of overweight. However, the association between SGA and overweight may be due to overcontrolling for body size. This study aimed to analyze the effect of controlling for child’s weight and height in the association between SGA and overweight in children born preterm. Methods Data were obtained from the Preterm Infant Multicenter Growth Study (n = 1089). The association between SGA and overweight at 36 months corrected age (CA) was analyzed using logistic regression models: 1) crude, 2) adjusted for baseline covariates, 3) adjusted for baselines covariates with additional adjustments separately for child’s weight and height at 21 months CA. Marginal structural models (MSM) with stabilized inverse probability weights were used to estimate the direct effect of SGA on overweight. Results The crude and adjusted models yielded a null association (OR, 95% CI: 0.88, 0.26-2.96; 0.95, 0.28-3.29). Adjusting for later height reversed the effect (OR, 95% CI: 2.31, 0.52-10.26), and adjusting for later weight reversed the effect and provided a significant association (OR, 95% CI: 6.60, 1.10-37.14). The MSMs with height and weight considered as mediators indicated no direct effect of SGA on overweight (OR, 95% CI: 0.83, 0.14-5.01; 0.71, 0.18-2.81). Conclusions Overcontrolling for body size can falsely induce an association between SGA and overweight. Key messages Mediators should not be treated as confounders.
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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 | 0.276 | 0.393 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".