Discrepant Inferences When Modeling Associations Between Time-Varying Exposures and Linear Growth Trajectories in Infancy Using Length-For-Age Z Scores Versus Raw Length
Bibliographic record
Abstract
To compare inferences from longitudinal models of the relationships between biomarkers of interest and linear growth outcomes in infancy using length-for-age z-scores (LAZ) based on age- and sex-specific growth standards, or raw length. This was a secondary analysis of data from a study of the association between bone-related biomarkers and infant linear growth trajectories up to 1 year of life in a subset of infants (n = 820) enrolled in the Maternal Vitamin D for Infant Growth trial. The linear growth outcome (LAZ or raw length) was modelled as a function of the interaction between each biomarker and age using linear mixed effect models with restricted cubic splines. Models were specified to obtain the best fit and reconcile discrepancies in results from LAZ and length models. Inferences from marginal effects at birth, 3 months, 6 months, and 12 months were compared, for a total 4 effect estimates from each of 10 pairs of LAZ and length models, resulting in 40 pairs of estimates. The following biomarkers were included: fibroblast growth factor 21 (FGF21), fibroblast growth factor 23 (FGF23), N-terminal propeptide of C-type natriuretic peptide (NT-proCNP), osteocalcin, osteoprotegerin, receptor activator of nuclear activator kappa-b ligand (RANKL), 25-hydroxyvitamin D (25OHD), C-reactive protein (CRP), Interleukin 6 (IL6), and insulin-like growth factor-1 (IGF1). Biomarkers were time-varying, measured in cord blood and at 3 and 6 months of age. The best fitting model for LAZ had 3 knots with random slopes, and the best fitting model for raw length had 5 knots, random slopes, and an exponential residual covariance structure. Comparisons of the pairs of marginal estimates from the LAZ vs length models resulted in discrepant inferences for 25% of effect estimates (10/40). Results were consistently concordant only for FGF23, 25OHD, CRP, and IL6. Length and LAZ represent the same biological construct but their use in longitudinal modelling may lead to different inferences about associations between time-varying exposures and infant growth, possibly due to residual confounding by age. These findings raise concerns about the reliability of studies of determinants or markers of infant linear growth based on longitudinal modelling of growth trajectories. Bill and Melinda Gates Foundation.
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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 | 0.150 | 0.383 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".