VP157 What Is The Response To Immuno-Oncology By Health Technology Assessment Agencies?
Bibliographic record
Abstract
INTRODUCTION: Immunotherapies are a relatively new innovative class of drug that have garnered excitement in the fight against cancer. In 2011, the immunotherapy drug, ipilimumab, was approved. Since then, four additional drugs have gained approval. This analysis evaluates the initial reception of immunotherapies by Health Technology Assessment (HTA) agencies. METHODS: The Context Matters Data Model was used to evaluate the regulatory and HTA agency decisions surrounding the five approved immunotherapies through November 2016: atezolizumab, elotuzumab, ipilimumab, nivolumab, and pembrolizumab. Thirty-three labels from Australia, Canada, Europe, and the United States, and ninety-two assessments from Agenzia Italiana del Farmaco (AIFA), Gemeinsamer Bundesausschuss (Federal Joint Committee; G-BA), Haute Autorité de Santé (French National Authority for Health; HAS), Institute for Clinical and Economic Review (ICER), Institute for Quality and Efficiency in Health Care (IQWiG), National Institute for Health and Care Excellence (NICE), Pharmaceutical Benefits Advisory Committee (PBAC), pan-Canadian Oncology Drug Review (pCODR), and Scottish Medicines Consortium (SMC) were found. Using a sample t-test and a chi-squared test, reimbursement agencies’ decisions were evaluated, and the clinical and economic factors that went into these decisions were examined. RESULTS: Of the evaluated reviews: sixty-four were for melanoma indications, fourteen were for non-small-cell lung cancer (NSCLC) indications, and seven were for kidney cancer indications. Many of the reviews did not reach any decision, but 75 percent of HTA decisions (n = 72; p = .0000) reached were positive. Elotuzumab, approved for multiple myeloma, received a positive decision from G-BA and a negative one from SMC. There was an association between different disease conditions or drugs and the rate of positive decisions. For reviews that had clinical reasons for their decisions, 72.9 percent (n = 59; p = .0000) had positive clinical rationales that were associated with positive decisions (p = .000). Economic rationales for decisions were more mixed, with only 48.4 percent (n = 31; p = .0000) receiving positive decisions. Positive economic evaluations were also associated with positive decisions (p = .000). Atezolizumab, approved only in the United States at the time of this writing, has yet to be reviewed by any of the HTA agencies. CONCLUSIONS: Immunotherapies are promising new options for the treatment of cancer. Thus far, reception by HTA agencies has generally been positive.
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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 | 0.086 | 0.320 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".