Age Bias in Time From Diagnosis Comparisons of Prostate Cancer Treatment
Bibliographic record
Abstract
OBJECTIVES: Observational studies of prostate cancer treatment have demonstrated a major survival benefit with prostatectomy; randomized trials have been less certain in this regard. This discrepancy is hypothesized to be due to the use survival calculations based on time from diagnosis (TFD), which can bias toward better survival for younger cohorts. Attained age is an alternative timescale that can mitigate this effect. A Surveillance, Epidemiology and End Results comparison of prostatectomy, radiotherapy (XRT), and conservative management for localized prostatic cancer was conducted to compare these 2 timescales. METHODS: Kaplan-Meier analysis was used to contrast overall survival based on TFD and attained age from 279,064 prostate cancer cases. Proportional hazards models were constructed and baseline hazard functions estimated. RESULTS: The prostatectomy cohort averaged 9 to 12 years younger than the radiotherapy or conservative management cohorts, and the baseline hazard depended more strongly upon age than TFD. Survival calculations based on TFD demonstrated a major benefit with prostatectomy compared with XRT and conservative management, consistent with prior observational studies. Calculations based on attained age, however, demonstrated lesser differences between treatment cohorts and were more consistent with published randomized trials. CONCLUSIONS: The survival benefit apparent to prostatectomy in conventional observational cohort studies could reflect an age-related bias attributable to their use of TFD analysis. Care is warranted in the choice of timescale in observational analysis if large age differences exist between treatment cohorts. Randomized controlled trials remain the most reliable means to compare prostate cancer treatments.
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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 | 0.149 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".