Identifying characteristics for a cost-effective psoriatic arthritis biomarker test: a development-focused health technology assessment
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate the required test characteristics that a psoriatic arthritis (PsA) biomarker test would need to achieve to be considered cost-effective. METHODS: We adapted an existing Markov model to compare a hypothetical biomarker with current practice. The model followed a patient cohort aged 45 years with moderate psoriasis (PsO) in which PsA was prevalent but unrecognized over a 40-year time horizon. Patients were assumed to be routinely seen at a dermatology clinic. In the current practice arm, patients with PsA were clinically detected. In the biomarker arm, a hypothetical test was assumed to be administered at baseline. Patients who screened positive would accept a combination of conventional disease-modifying antirheumatic drugs and targeted treatment to slow disease progression. Progression was modeled as linear changes in Health Assessment Questionnaire (HAQ) scores. We varied the sensitivity, specificity, and biomarker price based on current development progress. Scenario analyses considered alternative patient cohorts with mild and severe PsO separately. RESULTS: The base case showed that a biomarker test with 70 percent sensitivity, 80 percent specificity, and a price of US$500 would be cost-effective (incremental cost-effectiveness ratio US$47,566 per quality-adjusted life-year [QALY]). Three-way analyses showed that a test with 80 percent specificity could be cost-effective at a US$50,000 per QALY threshold with a sensitivity as low as 66 percent at US$500. Only a near-perfect test would be cost-effective at a US$1,000 price point. Results were sensitive to HAQ progression under treatment, therapy costs, and the patient population. CONCLUSION: This study supports the continued product development of candidate PsA biomarkers.
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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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".