Bias in Meta‐Analytic Modeling of Surrogate Endpoints in Cancer Screening Trials
James P Long, Abhishikta Roy, Ehsan Irajizad, Kim‐Anh Do, Yu Shen · 2026
Read Bias in Meta‐Analytic Modeling of Surrogate Endpoints in Cancer Screening Trials by James P Long, Abhishikta Roy, Ehsan Irajizad, Kim‐Anh Do, Yu Shen (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13549800.txt.
Description
A stand-in outcome can speed cancer screening trials, but judging the stand-in from past trials can mislead. James P Long and co-authors show how that bias creeps in, in a 2026 paper. Trials report estimates, not true values, for both the real outcome and the stand-in. That blur can make a weak stand-in look better than it is. The stand-in in focus is late-stage cancer counts, used in place of cancer deaths.
Math and simulation both showed the tilt toward rosy judgments. The team then rechecked ovarian cancer screening trials with the blur in view. Those trials, they found, say little about how good the stand-in really is. The authors urge that trial-level blur be built into such models from the start.
Catalog record
| CREATOR | James P Long, Abhishikta Roy, Ehsan Irajizad, Kim‐Anh Do, Yu Shen |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 37 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |