Estimating the effect of immortal‐time bias in urological research: a case example of testosterone‐replacement therapy
Bibliographic record
Abstract
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.
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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 | 0.272 | 0.567 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".