Time‐related biases in perinatal pharmacoepidemiology: A systematic review of observational studies
Bibliographic record
Abstract
BACKGROUND: Time-related biases, such as immortal time and time-window bias, frequently occur in pharmacoepidemiologic research. However, the prevalence of these biases in perinatal pharmacoepidemiology is not well understood. OBJECTIVE: To describe the frequency of time-related biases in observational studies of medications commonly used during pregnancy (antibiotic, antifungal, and antiemetic drugs) via systematic review. METHOD: We searched Medline and EMBASE for observational studies published between January 2013 and September 2020 and examining the association between antibiotic, antifungal, or antiemetic drugs and adverse pregnancy outcomes, including spontaneous abortion, stillbirth, preterm delivery, small-for-gestational age, pre-eclampsia, and gestational diabetes. The proportion of studies with time-related biases was estimated overall and by type (immortal time bias, time-window bias). RESULTS: Our systematic review included 20 studies (16 cohort studies, 3 nested case-control studies, and 1 case-control study), of which 12 examined antibiotic, 6 antiemetic, and 2 anti-fungal drugs. Eleven studies (55%) had immortal time bias due to the misclassification of unexposed, event-free person-time between cohort entry and exposure initiation as exposed. No included study had time-window bias. The direction of effect varied for both studies with and without time-related bias, with many studies reporting very wide confidence intervals around the effect estimates, thus making the direction of effect less interpretable. However, studies with time-related bias were more likely to show protective or null associations compared with studies without time-related bias. CONCLUSION: Time-related biases occur frequently in observational studies of drug effects during pregnancy. The use of appropriate study design and analytical approaches is needed to prevent time-related biases and ensure study validity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".