Promise and Pitfalls of the Sibling Comparison Design in Studies of Optimal Birth Spacing
Bibliographic record
Abstract
Numerous observational studies have shown that infants born after short interpregnancy intervals (the interval between birth and subsequent conception) are more likely to experience adverse perinatal outcomes than infants born following longer intervals. Yet it remains controversial whether the link between short interpregnancy interval and adverse outcomes is causal or is confounded by factors such as low socioeconomic position, inadequate access to health care, and unintended pregnancy. Sibling comparison studies, which use a woman as her own control by comparing exposure and outcome status of her different pregnancies (i.e., comparing sibling offspring), have gained popularity as a strategy to reduce confounding by these difficult-to-measure factors that are nevertheless relatively stable within women. A variant of this approach, used by Regan et al. (Am J Epidemiol. 2019;188(1):9–16) and reported in this issue of the Journal, is a maternally matched design based on a single interpregnancy interval per woman. Using real and simulated data, we highlight underappreciated shortcomings of these designs that could limit the validity of study findings. In particular, we illustrate how the single-interval variant appears to derive estimates from comparisons between different mothers, not within mothers. Future studies of optimal birth spacing using sibling comparison designs should examine in detail the potential consequences of these methodological limitations.
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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 | 0.548 | 0.637 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".