Quantitative bias analysis for time to event data needs good validation data

Clicks: 4
ID: 313710
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #296 of 314 articles by views in american journal of epidemiology

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 314 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Quantitative bias analysis offers an approach to estimating and adjusting for the impact of residual systematic error, yet to date, most methods have focused on binary outcomes, leaving methods for time-to-event outcomes underdeveloped. Misclassification in time-to-event analyses is challenging because both the events and person-time may contain errors which requires additional bias parameters. Recent work by Ackerman et al. (Am J Epidemiol. 0000;000(00):0000-0000) illustrates how methods for addressing misclassification of person-time outcomes can be used even when validation data are limited by using expert-informed ranges and simulation to define bias parameters. Although such approaches are better than qualitative assessments, they remain limited by lack of validation data, particularly data capturing person-time measurement error. Improving quantitative bias analysis will require emphasis on designing, conducting, and publishing validation studies that can generate generalizable estimates of misclassification parameters. It also requires methodological innovation and accessible software for person-time-based outcomes, including extensions of regression calibration and risk-based adjustment. Expanding quantitative bias analysis methods for time-to-event analyses will improve the validity, transparency, and interpretability of epidemiologic research and help ensure quantitative bias analysis becomes a routine component of study design and analysis.
Reference Key
openalex_W7160944132 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Matthew P Fox, R F M MacLehose
Journal american journal of epidemiology
Year 2026
DOI
10.1093/aje/kwag104
URL
https://doi.org/10.1093/aje/kwag104
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.