Methods for the assessment of selection bias in drug safety during pregnancy studies using electronic medical data
Bibliographic record
Abstract
Electronic health data are routinely used for population drug studies. Due to the ethical dilemma in carrying out experimental drug studies on pregnant women, the effects of medication usage during pregnancy on fetal and maternal outcomes are largely evaluated using this data collection medium. One major limitation in this type of study is the delayed inclusion of pregnancies in the cohort. For example, in the province of Quebec, Canada, a major pregnancy cohort only captured pregnancies after 20 weeks gestation. The purpose of this study was to demonstrate three methods that can be used to assess the extent of selection bias due to the delayed inclusion of pregnancies. We use causal directed acyclic graphs to explain the source of this selection bias. In an example involving a cohort of pregnant asthmatic women reconstructed from the linkage of administrative health databases from the province of Quebec, we use numerical derivations, a simulation study and a sensitivity analysis to investigate the potential for bias and loss of power due to the delayed inclusion. We find that this selection bias can be partially mitigated by controlling for variables related to (spontaneous or therapeutic) abortion and the outcome of interest. The three proposed methods allow for the pre and post hoc ascertainment of the bias. While delayed pregnancy inclusion selection bias (which includes "live birth bias") can produce substantial bias in pregnancy drug studies, all three methods are effective at producing estimates of the size of the bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.319 | 0.552 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".