Attention to Immortal Time Bias in Critical Care Research
Bibliographic record
Abstract
Observational studies in critical care medicine offer a popular and practical approach to questions of treatment effectiveness. Although observational research is widely understood to be susceptible to design and interpretation challenges, one well-described source of bias-immortal time bias (ITB)-is frequently present yet often overlooked. ITB may be introduced by study design oversights or mishandled during data analysis. When present, ITB can create inappropriate estimates of the benefit or harm of an exposure or intervention. Studies examining treatments in critically ill patients may be particularly susceptible to ITB, with consequences for clinical adoption and design and initiation of randomized trials. In this Critical Care Perspective, we illustrate the persistent problem of ITB in observational research using recent studies of hydrocortisone, ascorbic acid, and thiamine therapy in patients with sepsis and septic shock. Of the eight studies examined, none contained enough design or reporting elements to rule out the presence of ITB. To mitigate the influence of ITB in future observational studies, we present a novel checklist to help readers assess the features of study design, analysis, and reporting that introduce ITB or obscure its presence. We recommend that commonly used tools designed to evaluate observational research studies should include an ITB assessment.
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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 | 0.674 | 0.868 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.014 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".