Cost-utility analysis of infliximab and adalimumab for refractory ulcerative colitis
Bibliographic record
Abstract
OBJECTIVE: To evaluate cost-utility of infliximab and adalimumab for the treatment of moderate-to-severe ulcerative colitis (UC) refractory to conventional therapies in Canada. METHODS: A Markov model was constructed to evaluate incremental cost-utility ratios (ICUR) of 5 mg/kg and 10 mg/kg infliximab and adalimumab therapies compared to 'usual care' in treating a hypothetical cohort of patients (aged 40 years and weighing 80 kg) over a five-year time horizon from the perspective of a publicly-funded health care system. Clinical parameters were derived from the Active Ulcerative Colitis Trials 1 and 2. Costs were obtained through provincial drug benefit plans. ICUR was the main outcome measure and both deterministic and probabilistic sensitivity analyses were conducted. RESULTS: Compared to the strategy A ('usual care') in the base case analysis, the ICURs were CA$358,088/QALY for the strategy B ('5 mg/kg infliximab + adalimumab') and CA$575,540/QALY for the strategy C ('5 mg/kg and 10 mg/kg infliximab + adalimumab'). The results were sensitive to: the remission rates maintained in responders to 'usual care' and to 5 mg/kg infliximab, the rate of remission induced by adalimumab in non-responders to 5 mg/kg infliximab, early surgery rate, and utility values. When the willingness to pay (WTP) was less than CA$150,000/QALY, the probability of 'usual care' being the optimal strategy was 1.0. The probability of strategy B being optimal was 0.5 when the WTP approximated CA$400,000/QALY. CONCLUSIONS: The ICURs of anti-TNF-alpha drugs were not satisfactory in treating patients with moderate-to-severe refractory UC. Future research could be aimed at the long-term clinical benefits of these drugs, especially adalimumab for patients intolerant or unresponsive to infliximab treatment.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".