Oncology drug health technology assessment recommendations: Canadian versus UK experiences
Bibliographic record
Abstract
BACKGROUND: CANADA HAS TWO HEALTH TECHNOLOGY ASSESSMENT (HTA) AGENCIES RESPONSIBLE FOR ONCOLOGY DRUG FUNDING RECOMMENDATIONS: the Institut National d'Excellence en Santé et Services Sociaux (INESSS) for the province of Québec and the pan-Canadian Oncology Drug Review for the rest of Canada. The objective of the research was to review and compare the recommendations of these two agencies alongside an international comparator - the National Institute for Health and Care Excellence (NICE) in the United Kingdom - with respect to their recommendations records and the influence of clinical and cost-effectiveness evidence on the recommendations. METHODS: Recommendations were identified from the three agencies from January 1, 2002 to June 1, 2013. Recommendations were limited to five cancer sites (lung, breast, colon, kidney, blood) and to metastatic/advanced settings. Descriptive analyses examined the frequency of positive recommendations and factors related to a positive recommendation. For each recommendation, only publicly available information posted on the agency website was used to abstract data. RESULTS: There was a wide variation in the rate of positive recommendations, ranging from 48% for NICE to 95% for Canada's national process (among the 74% of its recommendations that were publicly posted). Interagency agreement was low, with full agreement for only six of the 14 drugs commonly reviewed by all three agencies. Evidence of a survival gain was not necessary for a positive recommendation; progression-free survival was acceptable. Different approaches were taken when addressing unacceptable cost-effectiveness. NICE was most likely to yield a negative recommendation on these grounds, whereas Canada's national process was most likely to yield a positive recommendation with a required pricing arrangement. CONCLUSION: In this analysis, the primary reason for the observed divergence between agency recommendations appeared to be the availability of mechanisms in each jurisdiction to address cost-effectiveness subsequent to the HTA assessment process. Furthermore, caution is needed when interpreting cross-agency comparisons between HTA agencies, as recommendations may not correspond directly to subsequent funding decisions and actual patient access. This may be a concern, given the high international profile of assessments conducted by the reviewed HTA agencies.
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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 | 0.093 | 0.384 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.024 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".