Differential treatment effect between younger and older adults for new cancer therapies in solid tumors supporting US Food and Drug Administration approval between 2010 and 2021
Bibliographic record
Abstract
BACKGROUND: Over one half of cancer diagnoses occur in patients aged 65 and older. The authors quantified how treatment effects differ between older and younger patients in oncology registration trials. METHODS: The authors performed a retrospective cohort study of registration trials supporting US Food and Drug Administration approval of cancer drugs (from January 2010 to December 2021). The primary outcome was differential treatment effect by age (younger than 65 years vs. 65 years or older) for progression-free survival and overall survival. Random effects meta-analysis and a pairwise comparison of outcomes by age group also were performed. RESULTS: Among 263 trials that met the inclusion criteria, 120 trials with 153 end points and 83,152 patients presented age-specific outcome data. Among the included randomized patients, 38% were aged 65 years and older compared with an incidence proportion of 55% in data from the National Cancer Institute's Surveillance, Epidemiology, and End Results program. Studies evaluating prostate cancer had the highest representation of patients aged 65 years or older (73%), whereas breast cancer studies had the lowest (20%). There were no changes in the proportion of patients aged 65 years or older over time (p = .86). Only 7% of end points showed a statistically significant interaction between outcome and age group. In a pooled analysis, there was an association between treatment effect and age for progression-free survival that approached but did not meet significance (hazard ratio, 0.95; p = .06), and there was no difference for overall survival (hazard ratio, 0.97; p = .79). CONCLUSIONS: Older adults remain under-represented in oncology registration trials. Significant differences in outcomes by age group were uncommon in individual trials and pooled analyses. However, clinical trial participants differ from real-world patients older than 65 years, and increased enrollment and ongoing research into differential treatment effects by age are needed.
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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 | 0.029 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".