A flexible approach to modelling stillbirths using the foetuses at risk approach
Bibliographic record
Abstract
BACKGROUND: Survival analysis methods are increasingly used to model the gestational age-specific risk of perinatal phenomena such as stillbirth. OBJECTIVES: To compare two types of survival analysis models, and highlight differences by estimating the relationships between pre-pregnancy BMI and gestational age-specific rates of stillbirth. METHODS: The study was based on singleton live births and stillbirths in the United States in 2016-2017, with data obtained from the natality and fetal death files of the National Center for Health Statistics. We compared Cox regression versus piecewise exponential additive mixed models (PAMMs) for modelling the relationship between BMI and stillbirth across gestational age. In a second analysis, we illustrated the performance of both models for assessing the relationship between the trimester-specific number of cigarettes smoked, a time-dependent covariate, and stillbirth. RESULTS: The study population included 7,567,316 births, of which 42,739 were stillbirths (5.6 per 1000 total births). Stillbirth rates increased with increasing pre-pregnancy BMI and increasing gestational age. In analyses with BMI as a categorical variable, the Cox model and PAMM models yielded similar results. Analyses of BMI as a continuous variable also showed similar results when BMI associations were assumed to be linear, and the changes in gestational age-specific rates were modelled parametrically. However, results differed slightly when PAMMs, modelled with data-driven approaches, were used to estimate changes in BMI effects across gestational age; PAMMs provided a more nuanced modelling of time-varying effects. PAMM models showed an approximately linear increase in the effect of smoking on stillbirth with increasing gestational age. CONCLUSIONS: For survival analyses using the foetuses-at-risk approach, PAMMs provide a valuable alternative to the traditional Cox model, with increased modelling flexibility when proportional hazards assumptions are violated.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".