The impact of statistical properties of incremental monetary net benefit and incremental cost-effectiveness ratio on health economic modeling choices
Bibliographic record
Abstract
There is controversy on whether to use incremental monetary net benefit (INMB) or incremental cost-effectiveness ratio (ICER) in health economic evaluations alongside randomized controlled trials. We studied the impact of restricted mean survival time (RMST) on the long-term projection of INMB and ICER. We analyzed the unbiasedness and efficiency of ICER and INMB by (1) deriving the metrics' expected values and variances based on theoretical probability distributions, (2) simulating their 15-year post-trial projections based on between-arm-RMST-gained through a 2 × 4 × 2 factorial experiment of Markov 2-state microsimulations. Simulations and comparison were run on the data from the Cardiovascular Outcomes for People Using Anticoagulation Strategies Study (COMPASS). Our simulation findings using RMST showed that ICER was more efficient than INMB, regardless of disease populations, time horizon, modeling choices, and underlying probability distributions of incremental mean cost and effect. ICER had a small variance and thus showed its robustness to the choices of models. INMB is unbiased, while ICER is biased-but-remediable. INMB’s variance varies with a willingness-to-pay (WTP) threshold value quadratically while ICER’s variance varies with incremental-mean-cost quadratically. A simple and naïve model can sufficiently estimate ICER. Future metrics are expected to be health-economic-meaningful, unambiguous, unbiased, efficient, and statistical-inference-friendly.
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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 | 0.235 | 0.553 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".