Bias in case‐crossover studies of medications due to persistent use: A simulation study
Bibliographic record
Abstract
PURPOSE: The case-crossover design is increasingly used to evaluate the effects of chronic medications; however, as traditionally implemented in pharmacoepidemiology, with referent period preceding the outcome, it may lead to bias in the presence of persistent exposures. We aimed to evaluate the extent and magnitude of bias in case-crossover analyses of chronic and persistent exposures, using simulations. METHODS: We simulated cohorts with either 30-day, 180-day, or 2-year exposure duration; and with varying degrees of persistence (10%, 30%, 50%, 70%, or 90% of patients not stopping exposure). We evaluated all scenarios under the null and the scenario with 30% persistence under varying exposure effects (odds ratios of 0.25 to 4.0). Cohorts were analyzed using conditional logistic regression that compared the odds of exposure on the outcome day to the odds of exposure on a referent day 30 days prior to the outcome. We further implemented the case-time-control design to evaluate its ability to adjust for bias from persistence. RESULTS: Case-crossover analyses produced unbiased estimates across all scenarios without persistent users, regardless of exposure duration. In scenarios where some patients persisted on treatment, case-crossover analyses resulted in upward bias, which increased with increasing proportion of persistent users, but did not vary substantially in relation to the magnitude of the true effect. Case-time-control analyses removed bias in all scenarios. CONCLUSIONS: Investigators should be aware of bias due to treatment persistence in unidirectional case-crossover analyses of chronic medications, which can be remedied with a control group of similarly persistent noncases.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".