Bias within economic evaluations – the impact of considering the future entry of lower-cost generics on currently estimated incremental cost-effectiveness ratios of a new drug
Bibliographic record
Abstract
Jason R Guertin,1,2 Dominic Mitchell,1,3 Farzad Ali,4 Jacques LeLorier1 1CHUM Research Center, Montréal, QC, 2Programs for Assessment of Health Technology in Health Research Institute, Hamilton, ON, 3Logimétrix Inc., Repentigny, 4Pfizer Canada Inc., Kirkland, QC, Canada Background: Most economic evaluation models compare a new patented drug (NPRx) to a generic comparator. Drug costs within these models are usually limited to the retail cost of both drugs at the time of model conception. However, the retail cost of the NPRx is expected to drop once generic versions of this molecule are introduced following the expiration of the NPRx’s patent. The objective of this study was to examine the impact on the incremental cost-effectiveness ratio (ICER) of the future introduction of lower-cost generic versions of the NPRx within the model’s time horizon. Methods: We examined the impact of this parameter with the use of two approaches: 1) a mathematical proof identifying its impact on the NPRx’s ICER; and 2) applying this parameter to a previously published economic model comparing a NPRx to a generic comparator and identifying what would have been the NPRx’s ICER had this model considered this parameter. Results: As expected, both the mathematical proof and the application to the previously published economic model showed that considering the future introduction of lower-cost generic versions of the NPRx within the model’s time horizon lowers the NPRx’s ICER. The timing of the future entry of lower-cost generic molecules, their relative price compared to that of the patented version, and the discount rate applied to future costs all influenced the results. Conclusion: An ICER estimated within economic evaluations comparing NPRx to generic comparators which ignore the future introduction of lower-cost generic versions of the NPRx within the model’s time horizon will tend to be overestimated. Inclusion of this parameter should be considered within future economic evaluations. Keywords: loss of patent exclusivity, health economics, cost and cost analysisA Letter to the Editor has been published for this paper
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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 | 0.321 | 0.609 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".