Abstract 11521: Prevalence of Immortal Time Bias in High-Impact Cardiovascular Publications
Bibliographic record
Abstract
Background: Immortal time bias (ITB) is a consequence of non-uniform time zeros (ie. start of follow-up) between treatment groups, resulting in misclassification or selection bias in observational cohort studies. ITB poses a substantial problem as its presence favors the treatment, leading to an overestimation of the protective effect or an underestimation of the harmful effects of the treatment group compared to the control group. Depending on the amount of person-time misclassified or excluded, the magnitude of bias due to ITB may be substantially greater than other biases, such as confounding. Objective: To determine the prevalence of ITB in observational cohort studies published between 2020 and 2021 in high-impact cardiovascular journals. Methods: Observational cohort studies published in Circulation, European Heart Journal, and the Journal of the American College of Cardiology between January 2020 and December 2021 were screened for inclusion. Cross-sectional studies, case series, case-control, time-series analyses, and survey or surveillance studies were excluded. Two independent reviewers (ie. epidemiologists) evaluated each article for the presence and type of ITB. Results: Of 1,558 screen articles, 154 published articles were eligible for inclusion. Twenty of 154 (13%) publications had ITB present. ITB was most frequently due to misclassification bias (17 of 20 articles, 85%). ITB due to selection bias was present in 5 of 20 (25%) articles. Two articles had ITB due to both misclassification and selection biases. Most studies (75%) with ITB did not have an active comparator group. Among studies with ITB, event-based cohorts were the most frequent (65%), followed by event-exposure based cohorts (15%), and exposure-based (10%) and time-based (10%) cohorts. ITB was present in studies with various exposures, including medications (7), surgeries or procedures (5), devices (4), and diseases (4). Conclusion: A substantial proportion (13%) of published observational cohort studies in high-impact cardiovascular journals had ITB present and may result in an overestimation of treatment effect. As ITB is preventable with study design techniques, researchers need to be cognizant of this bias.
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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 | 0.145 | 0.546 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.053 | 0.053 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".