Applying a multiverse approach in ReACH cohort studies to examine the impact of Prenatal Alcohol Exposure data harmonization and analytical choices on research findings
Bibliographic record
Abstract
Pooling individual participant data from multiple studies can increase power to detect effects. However, when studies use different measures for the same constructs, harmonizing measures of that construct should precede statistical analysis on pooled data. A multiverse analysis conducts the desired analysis across all available datasets that could be obtained (e.g., using different definitions of constructs in harmonization), thus enabling the comparison of results across choice combinations. The present paper explores the impact of harmonization decisions using a case study. Data from four Canadian studies available through Research Advancement through Cohort Cataloguing and Harmonization (ReACH) are used to assess whether Prenatal Alcohol Exposure (PAE) is predictive of low birthweight (LBW) for gestational age (GA). We focused on variations within two dimensions: PAE definition and statistical model. Analyses controlled for smoking, gestational hypertension, gestational diabetes, ethnicity, income, and age. Results indicate that conclusions about the relationships between PAE and LBW controlling for covariates depend on the definition of PAE, the choice of the statistical method, and timeframe. We discuss possible causes of variability in conclusions and how multiverse analysis can be used to assess the robustness of conclusions in data synthesis, following harmonization for other research questions in epidemiology
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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 | 0.442 | 0.577 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".