Shifting perspectives on the value of non-OS endpoints and PROs: Considerations across stakeholder groups to support oncology HTA decision-making.
Bibliographic record
Abstract
e13646 Background: The ability of novel cancer drugs to extend patients’ overall survival (OS) has been key to determining their approval and access conditions. However, in some disease settings, OS can have limitations: delayed access due to the time required to collect mature OS data; confounding from subsequent treatment lines; and poor ability to reflect value beyond survival. Stakeholders such as regulators and physicians increasingly recognise the value of non-OS endpoints as surrogates for OS. In addition, non-OS endpoints and patient reported outcomes (PROs) may have stand-alone value, e.g., in capturing disease burden. However, stakeholders are misaligned on the benefits of non-OS endpoints and PROs due to differences in underlying value drivers. Methods: This study investigates stakeholder perceptions of non-OS endpoints and PROs, based on 76 semi-structured interviews with physicians, patients, patient advocacy groups (PAGs), regulators and health economists in 7 key markets (U.S., Canada, Japan, UK, France, Germany and Italy), 3 roundtable discussions and a literature review. Results: Non-OS endpoints address some concerns with OS as they can be captured earlier in trials and may act as surrogates of OS; show reduced susceptibility to confounding; and better reflect the value to patients beyond survival. However, the value perception of non-OS endpoints and PROs varies across stakeholders: 1) Patients / PAGs value non-OS endpoints as they capture outcomes beyond mortality, e.g., the avoidance of surgery and PROs such as pain. 2) Physicians acknowledge the scientific and clinical trial limitations of relying primarily on OS, but note that OS limitations vary by cancer type / stage. 3) Regulators are accepting of non-OS endpoints, providing accelerated / conditional approvals when they are used as surrogates for OS or when they have standalone value, and increasingly expect PROs in submission dossiers. 4) Payers are uncertain, as without appropriate validation, drugs approved based on non-OS endpoints risk additional treatment burden and cost to healthcare systems without patient benefit. The limited alignment in value perception among stakeholders is further complicated as this may shift during the course of the disease – e.g., patients at early stages may value treatments which prolong survival but may place more emphasis on quality of life and freedom from progression as the disease advances. Conclusions: The value of non-OS endpoints and PROs is increasingly recognised, particularly by physicians and regulators; payers, however, can be less accepting, driven largely by uncertainties around their value, as surrogates or as stand-alone measures of benefit. Stakeholders must align on the value of non-OS endpoints and PROs, ensuring they are fit for purpose for each cancer type / stage, to improve their acceptance and advance patient access to novel 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 | 0.158 | 0.203 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".