Sensitivity analysis for live birth bias in the Ulaanbaatar Gestation and Air Pollution Research study
Bibliographic record
Abstract
The Ulaanbaatar Gestation and Air Pollution Research (UGAAR) study is a randomized controlled trial of the effects of portable high efficiency particulate air (HEPA) filter air cleaner use during pregnancy on fetal growth and child health outcomes. A challenge with the study is that the outcome variables cannot be measured in the absence of a live birth. Thus pregnancy loss is a competing event for the outcome variables that can obscure the intervention-outcome relationship. This phenomenon is called "live birth bias" in the epidemiological literature, and it is an example of selection bias due to adjustment for variables affected by previous exposure. Methods: In this investigation, we reanalyzed data from the UGAAR study and examined the impacts of the intervention on three health outcomes: preterm birth (PTB), birth weight, and full-scale IQ (FSIQ) measured with the Wechsler Preschool and Primary Scale of Intelligence III when children were four years old, while accounting for live birth bias. Specifically, we used a novel multiple imputation approach to simulate scenarios in which pregnancy losses had instead been born alive and experienced a range of health outcomes. Results: After accounting for live birth bias, the association between the intervention and PTB diminished. Additionally, the magnitude of intervention effect on birth weight and FSIQ increased. FSIQ was less sensitive to live birth bias than birth weight. Conclusion: We introduced a novel analysis approach to examine the role of live birth bias, and the findings will be useful in environmental epidemiology studies of birth cohorts.
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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 | 0.303 | 0.395 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.015 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".