Assessing the efficacy‐effectiveness gap for cancer therapies: A comparison of overall survival and toxicity between clinical trial and population‐based, real‐world data for contemporary parenteral cancer therapeutics
Bibliographic record
Abstract
BACKGROUND: Although increasing evidence has suggested that an efficacy-effectiveness gap exists between clinical trial (CT) and real-world evidence (RWE), to the authors' knowledge, the magnitude of this difference remains undercharacterized. The objective of the current study was to quantify the magnitude of survival and toxicity differences between CT and RWE for contemporary cancer systemic therapies. METHODS: Patients receiving cancer therapies funded under Cancer Care Ontario's New Drug Funding Program (NDFP) were identified. Landmark CTs with data regarding survival and adverse events (AEs) for each drug indication were identified. RWE for survival and hospitalization rates during treatment were ascertained through Canadian population-based databases. The efficacy-effectiveness gap for each drug indication was calculated as the difference between RWE and CT data for median overall survival (OS), 1-year OS, and generated hazard ratios (HRs) with 95% CIs from Kaplan-Meier OS curves. Toxicity differences were calculated as the difference between RWE of hospitalization rates and CT serious AE rates. RESULTS: Twenty-nine indications from 20 systemic therapies were included. Twenty-eight of 29 indications (97%) demonstrated worse survival in RWE, with a median OS difference of 5.2 months (interquartile range, 3.0-12.1 months). Lower effectiveness in RWE also was demonstrated through a meta-analysis of an OS hazard ratio of 1.58 (95% CI, 1.39-1.80). The median difference between RWE for hospitalization rates and CT serious AEs was 14% (95% CI, 9%-22%). CONCLUSIONS: An efficacy-effectiveness gap exists for contemporary cancer systemic therapies, with a 5.2-month lower median OS observed in RWE compared with CT data. These data supports the use of RWE to better inform real-world decision making regarding the use of cancer systemic therapies.
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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 | 0.222 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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; 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".