Understanding and Avoiding Immortal-Time Bias in Gastrointestinal Observational Research
Bibliographic record
Abstract
Pharmacoepidemiologic analyses, in which observational data is interrogated to evaluate relationships between patterns of drug use and both beneficial and adverse outcomes, are being increasingly used in the study of inflammatory bowel disease. However, the results of many of these analyses may be corrupted by the presence of immortal person-time bias, an analytic error which can result in an overestimation of the benefits of medical therapy. In this report, we will describe immortal person-time bias, explain the mechanism through which it confers a false benefit, and guide the reader in how to identify this source of bias in the medical literature.
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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 | 0.373 | 0.739 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".