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Record W2617620642 · doi:10.1111/bju.13918

Estimating the effect of immortal‐time bias in urological research: a case example of testosterone‐replacement therapy

2017· review· en· W2617620642 on OpenAlexafffundabout
Christopher J.D. Wallis, Refik Saskin, Steven A. Narod, Calvin Law, Girish S. Kulkarni, Arun Seth, Robert K. Nam

Bibliographic record

VenueBritish Journal of Urology · 2017
Typereview
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsUniversity Health NetworkHealth Sciences CentreUniversity of TorontoInstitute for Work & HealthSunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineHazard ratioConfidence intervalObservational studyCohort studyPopulationProportional hazards modelInternal medicineCohortSurvival analysisMeta-analysisDemographyTestosterone (patch)Retrospective cohort studyOncologyEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To quantify the effect of immortal-time bias in an observational study examining the effect of cumulative testosterone exposure on mortality. PATIENTS AND METHODS: We used a population-based matched cohort study of men aged ≥66 years, newly treated with testosterone-replacement therapy (TRT), and matched-controls from 2007 to 2012 in Ontario, Canada to quantify the effects of immortal-time bias. We used generalised estimating equations to determine the association between cumulative TRT exposure and mortality. Results produced by models using time-fixed and time-varying exposures were compared. Further, we undertook a systematic review of PubMed to identify studies addressing immortal-time bias or time-varying exposures in the urological literature and qualitatively summated these. RESULTS: Among 10 311 TRT-exposed men and 28 029 controls, the use of a time-varying exposure resulted in the attenuation of treatment effects compared with an analysis that did not account for immortal-time bias. While both analyses showed a decreased risk of death for patients in the highest tertile of TRT exposure, the effect was overestimated when using a time-fixed analysis (adjusted hazard ratio [aHR] 0.56, 95% confidence interval [CI]: 0.52-0.61) when compared to a time-varying analysis (aHR 0.67, 95% CI: 0.62-0.73). Of the 1 241 studies employing survival analysis identified in the literature, nine manuscripts met criteria for inclusion. Of these, five used a time-varying analytical method. Each of these was a large, population-based retrospective cohort study assessing potential harms of pharmacological agents. CONCLUSIONS: Where exposures vary over time, a time-varying exposure is necessary to draw meaningful conclusions. Failure to use a time-varying analysis will result in overestimation of a beneficial effect. However, time-varying exposures are uncommonly utilised among manuscripts published in prominent urological journals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.272
metaresearch head score (Gemma)0.567
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score0.897

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2720.567
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.009
Bibliometrics0.0060.010
Science and technology studies0.0020.007
Scholarly communication0.0060.006
Open science0.0030.004
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.313
GPT teacher head0.449
Teacher spread0.136 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSimulation or modeling
DomainMethods
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

Citations9
Published2017
Admission routes3
Has abstractyes

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