Economic evaluation of prostate cancer risk assessment methods: A cost‐effectiveness analysis using population data
Bibliographic record
Abstract
BACKGROUND: The current prostate cancer (PCa) screening standard of care (SOC) leads to unnecessary biopsies and overtreatment because decisions are guided by prostate-specific antigen (PSA) levels, which have low specificity in the gray zone (3-10 ng/mL). New risk assessment tools (RATs) aim to improve biopsy decision-making. We constructed a modeling framework to assess new RATs in men with gray zone PSA from the British Columbia healthcare system's perspective. METHODS: We evaluated the cost-effectiveness of a new RAT used in biopsy-naïve men aged 50+ with a PSA of 3-10 ng/mL using a time-dependent state-transition model. The model was informed by engaging patient partners and using linked administrative health data, supplemented with published literature. The incremental cost-effectiveness ratio and the probability of the RAT being cost-effective were calculated. Probabilistic analysis was used to assess parameter uncertainty. RESULTS: In the base case, a RAT based on an existing biomarker's characteristics was a dominant strategy associated with a cost savings of $44 and a quality-adjusted life years (QALY) gain of 0.00253 over 18 years of follow-up. At a cost-effectiveness threshold of $50,000/QALY, the probability that using a RAT is cost-effective relative to the SOC was 73%. Outcomes were sensitive to RAT costs and accuracy, especially the detection rate of high-grade PCa. Results were also impacted by PCa prevalence and assumptions about undetected PCa survival. CONCLUSIONS: Our findings showed that a more accurate RAT to guide biopsy can be cost-effective. Our proposed general model can be used to analyze the cost-effectiveness of any novel RAT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".