Time-window Bias in Case-control Studies
Bibliographic record
Abstract
Time-related biases in cohort studies can produce illusory "beneficial" effects of medications due entirely to an artifact of the analytic design. We describe "time-window bias" in the context of a case-control study, reporting that statin use was associated with a 45% reduction in the incidence of lung cancer. This bias results from the use of time-windows of different lengths between cases and controls to define time-dependent exposures. We illustrate the bias using a population of 365,467 patients from the United Kingdom's General Practice Research Database, including 1786 incident cases of lung cancer during 1998-2004. The case-control approach used in the published study yielded a rate ratio of lung cancer incidence of 0.62 with statin use (95% confidence interval = 0.55-0.71). A case-control approach that properly accounts for time produces a rate ratio of 0.99 (0.85-1.16)-suggesting no benefit of statins on lung cancer risk. We show analytically that the magnitude of the bias is proportional to the ratio of the unequal time-window lengths.
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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 | 0.386 | 0.654 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".