Case‐crossover study design in pharmacoepidemiology: systematic review and recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.020 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it