Case‐crossover study design in pharmacoepidemiology: systematic review and recommendations
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to systematically identify and review articles that use the case-crossover study design in the area of pharmacoepidemiology. METHODS: A systematic search of MEDLINE® (Ovid Technologies, New York City, NY, USA), EMBASE® (Elsevier Inc., Philadelphia, PA, USA), and Web of Science® (Thomson Reuters, New York City, NY, USA) was completed to identify all English language articles that applied the case-crossover study design in the area of pharmacoepidemiology. The number of reviews, methodological contributions, and empirical pharmacoepidemiologic applications were summarized by publication year. Empirical applications were retrieved, and methodological details (outcome, exposure, exposure windows, sensitivity analysis, statistical reporting) were tabulated and compared to methodological recommendations based on exposure characteristics, exposure windows, and discordant pairs data display. RESULTS: Of 836 unique articles identified, 99 pharmacoepidemiologic studies were eligible: 20 methodological contributions, 9 review papers, and 70 empirical applications. Only three empirical applications in the area of pharmacoepidemiology were published before 2000. Since 2000, the number of empirical pharmacoepidemiologic applications published annually has generally increased over time, to before a high of 15 published in 2011. The design was mainly applied to examine drug safety (96%), and most applications investigated: psychotropic (24%) and analgesic (17%) exposure drug classes; and considered hospitalization (23%) and cardiovascular/cerebrovascular (21%) events. Only 31% of applications displayed sufficient data to enable readers to confirm odds ratios presented. CONCLUSIONS: Use of the case-crossover design in pharmacoepidemiology has increased rapidly in the last decade. As the application of the case-crossover design continues to increase, it is important to develop standards of practice, especially for display of data.
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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 | 0.150 | 0.343 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".