Economic Evaluation Methods in Oncology
Bibliographic record
Abstract
To fill a gap in the literature and to better inform decision making in oncology, this doctoral thesis investigates the role and impact of analytical methods in the economic evaluation of oncology medications through three main chapters which have been recently published. Chapter 2 presents a systematic literature survey of published economic evaluations in oncology over a 10-year period in order to identify, examine, and describe analytical methods that have been utilized (published in Pharmacoeconomics Open in 2021). This chapter demonstrated that greater detail in reporting of extrapolation methods, statistical techniques, and validation procedures is needed in order to conform with best practices outlined in existing economic evaluation guidelines. Chapter 3 complements the work of chapter 2 but takes a different perspective through an examination of the methods reported in economic evaluations published by HTA agencies in Canada, the UK, and Australia (published in Current Oncology in 2022). This chapter revealed significant reporting discrepancies across the agencies and concluded that common standards for reporting the results of HTAs should be implemented. Building on chapters 2 and 3, chapter 4 provides a model-based health technology re-assessment of an oncology drug approved on the basis of interim trial data using recently published long-term follow up data (published in Current Oncology in 2023). The findings from this chapter highlight the importance of transparency in the reporting of methods, the impact of using a life-cycle approach to HTA, and demonstrate the existence of a tradeoff between clinical/economic uncertainty and the value of the incremental cost-effectiveness ratio (ICER). The final chapter provides the overall conclusions of the research and presents avenues for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.116 | 0.245 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".