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Abstract 11521: Prevalence of Immortal Time Bias in High-Impact Cardiovascular Publications

2022· article· en· W4380794611 on OpenAlexaff
Michelle Samuel, Stan Kutcher, Leah K. Flatman, Buajitti Emmalin, Farida Mahmoud, Saruchi Bandargal

Bibliographic record

VenueCirculation · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsMcGill UniversityMontreal Heart Institute
Fundersnot available
KeywordsMedicineObservational studyConfoundingSelection biasCohortCohort studyInformation biasPediatricsInternal medicinePathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.292
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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