Will the Markov model and partitioned survival model lead to different results? A review of recent economic evidence of cancer treatments
Bibliographic record
Abstract
Introduction: Balancing the high cost of treatment brought about by new therapies has become a problem that needs to be considered. Cost-effectiveness analysis (CEA) is a commonly used method that provides information on the potential value of new cancer treatments. The Markov and partitioned survival (PS) models are commonly used. Whether the results differ between the models in empirical research and the methodological differences remain unclear.Areas covered: A review was conducted to identify Canadian Agency for Drugs and Technologies in Health (CADTH) reports and papers published during the past 5 years that reported full economic evaluations of cancer treatments and used both models. In the included studies, most results except one obtained using the two models did not significantly differ.Expert opinion: Not enough evidence could support that there existed relevant bias in empirical studies about the PS model, and more methodological research and application of empirical research should be performed. We recommended that when individual data are available and the model structure is not complicated, the PS model is more appropriate. Both the PS and Markov models are recommended to assess model structure uncertainty.
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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".