A Microsimulation Model to Determine the Cost-Effectiveness of Treat-to-Target Strategies for Crohn's Disease
Bibliographic record
Abstract
INTRODUCTION: Cost-effectiveness of biomarker- vs endoscopy-based treat-to-target monitoring in Crohn's disease (CD) is unknown. METHODS: A microsimulation model for CD was built to simulate biomarker (fecal calprotectin) vs endoscopy-based monitoring in a treat-to-target fashion. Published literature in combination with patient-level data from phase 3 clinical trials and population estimates for therapeutic drug monitoring were used to generate transition probabilities, costs, and utilities. Tracker variables were used to modify downstream probabilities and outcomes based on previous exposures, response patterns, and disease-related complications or surgery history. The primary outcome was cost-effectiveness over a 5-year horizon at a willingness-to-pay threshold of $100,000/quality-adjusted life-year. Probabilistic sensitivity analyses in addition to multiple 1-, 2-, and 3-way microsimulation sensitivity analyses were performed. RESULTS: In the base-case model, the endoscopy-based monitoring strategy dominated the biomarker-based monitoring strategy over a 5-year horizon. Over shorter periods of observation, the biomarker-based monitoring strategy became progressively more cost-effective, with cost-effectiveness achieved for this strategy over a 1-year horizon. Therapeutic drug monitoring did not influence short-term cost-effectiveness of biomarker-based monitoring. Once in endoscopic remission, continued biomarker-based vs endoscopy-based monitoring was more cost-effective. A hybrid biomarker-endoscopy-based monitoring strategy dominated the endoscopy-based monitoring strategy over a 5-year horizon. The strongest determinants for cost-effectiveness were cost of colonoscopy and diagnostic performance of fecal calprotectin. DISCUSSION: The most cost-effective approach for treat-to-target monitoring in CD is up-front biomarker-based monitoring followed by endoscopy-based monitoring if not in endoscopic remission by 1 year and then returning to biomarker-based monitoring once in endoscopic remission.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".