Impact of value frameworks on the magnitude of clinical benefit: Evaluating a decade of randomized trials for systemic therapy in solid malignancies.
Bibliographic record
Abstract
e19410 Background: In the era of rapid development of new, expensive cancer therapies, value frameworks were developed to quantify clinical benefit. We assessed the evolution of the magnitude of clinical benefit since the 2015 introduction of the ASCO and ESMO value frameworks. Methods: Randomized phase II and III clinical trials assessing systemic therapies for solid malignancies from January 2010 to July 2019 were evaluated. Study characteristics were recorded, and magnitude of clinical benefit (Δ) was calculated for the endpoints overall survival (OS), progression-free survival (PFS), response rate (RR), and quality of life (QoL). Multivariable analyses compared ΔOS, ΔPFS, and ΔRR in 2010-2014 [pre-value frameworks (PRE)] to 2015-2019 [post-value frameworks (POST)]. Results: In the 290 studies analyzed [60 (21%) PRE and 230 (79%) POST], the most common primary endpoint was PFS (46%), followed by OS (20%), RR (16%), and QoL (8%), with a non-significant increase in OS and decrease in RR as a primary endpoint in the POST era (Table). Studies evaluating immunotherapy and palliative therapy significantly increased POST [0 (0%) v 39 (17%), Fisher’s exact p<0.01 and 25 (42%) v 142 (62%), Chi squared p=0.01, respectively]. Studies reporting improvement in QoL doubled POST [3 (5%) v 22 (10%) Fisher’s exact p=0.56], however not statistically significant. Median ΔOS was significantly greater POST (N= 140 evaluable studies, 1.3 v -0.2 months, Wilcoxon p=0.005) but there was no significant difference in median ΔPFS or ΔRR. Multivariable analyses revealed significant improvement in ΔOS POST (OR 3.08, 95% CI 0.54-5.62, p=0.02) while adjusting for drug mechanism of action, line of therapy, disease setting, and primary endpoint. Conclusions: After the development of value frameworks, median OS improved minimally. The impact of value frameworks has yet to be fully realized in randomized clinical trials. Efforts to include endpoints shown to impact value, such as QoL, into clinical trials are warranted. [Table: see text]
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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 | 0.349 | 0.390 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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; 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".