Quantifying the impact of immortal time bias: empirical evidence from meta-analyses
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
Objectives Immortal time bias (ITB) occurs when a period during which, by design, participants cannot experience the outcome (like death) is incorrectly included in the treatment group’s follow-up, artificially making the treatment look better than it truly is. We aimed to identify a systematic sample of cases of ITB in the literature of studies using survival analysis and assess the impact of ITB on the results. Design Meta-epidemiological study (PROSPERO[CRD42022356073]). Setting We searched PubMed/MEDLINE, Embase and Cochrane Database of Systematic Reviews from database inception to August 2024. Systematic reviews with quantitative syntheses that allowed subgroup analysis by the presence of ITB for any available exposure-outcome pairs (‘topics’) were eligible for inclusion. Participants Participants included in the systematic reviews. Main outcome measures Information on ITB and effect sizes (ESs) with 95% confidence interval for individual studies in forest plots were extracted to run re-analysis using generic inverse variance fixed- and random-effects methods. After extracting data, we conducted subgroup analysis by the presence of ITB for all available topics and assessed the impact of ITB on the heterogeneity ( I 2 ), vulnerability of evidence (or conclusion), statistical significance of the finding, and altering ES in favour of intervention/exposure. Results The median (interquartile range (IQR)) number of studies included for a topic was 6 (4–10). Across 25 topics (including 182 studies), 44.0% of the eligible studies (80 studies) were affected by ITB. Among the 21 topics where both studies with ITB and studies without ITB were available (four topics only had studies unaffected by ITB), 57.1% (12/21) demonstrated statistically significant results only in studies with ITB ( n = 11 topics) or only in studies without ITB (one topic). In 23.8% (5/21), the overall summary results changed from statistically significant to non-statistically significant or vice versa after excluding studies with ITB. The ratio of ES – summary ES from studies with ITB relative to summary ES from studies without ITB – was 0.71 (95% CI, 0.66-0.78), suggesting that the ES from studies with ITB was larger by an average of 29% in favour of the intervention/exposure. Excluding studies involving ITB reduced between-study heterogeneity ( I 2 ) by 21.4% on average. Conclusions ITB can be common among studies in some medical areas, and its presence may substantially inflate the ESs and lead to misleading, exaggerated evidence.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.122 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.016 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it