Cost-Effectiveness of Duloxetine in Chronic Low Back Pain
Bibliographic record
Abstract
STUDY DESIGN: Cost-effectiveness model from a Quebec societal perspective using meta-analyses of clinical trials. OBJECTIVE: To evaluate the cost-effectiveness of duloxetine in chronic low back pain (CLBP) compared with other post-first-line oral medications. SUMMARY OF BACKGROUND DATA: Duloxetine has recently received a CLBP indication in Canada. The cost-effectiveness of duloxetine and other oral medications has not previously been evaluated for CLBP. METHODS: A Markov model was created on the basis of the economic model documented in the 2008 osteoarthritis clinical guidelines of the National Institute for Health and Clinical Excellence. Treatment-specific utilities were estimated via a meta-analysis of CLBP clinical trials and a transfer-to-utility regression estimated from duloxetine CLBP trial data. Adverse event rates of comparator treatments were taken from the National Institute for Health and Clinical Excellence model or estimated by a meta-analysis of clinical trials in osteoarthritis using a maximum-likelihood simulation technique. Costs were developed primarily from Quebec and Ontario public sources as well as the published literature and expert opinion. The 6 comparators were celecoxib, naproxen, amitriptyline, pregabalin, hydromorphone, and oxycodone. Subgroup analyses and 1-way and probabilistic sensitivity analyses were performed. RESULTS: In the base case, naproxen, celecoxib, and duloxetine were on the cost-effectiveness frontier, with naproxen the least expensive medication, celecoxib with an incremental cost-effectiveness ratio of $19,881, and duloxetine with an incremental cost-effectiveness ratio of $43,437. Other comparators were dominated. Key drivers included the rates of cardiovascular and gastrointestinal adverse events and proton pump inhibitor usage. In subgroup analysis, the incremental cost-effectiveness ratio for duloxetine fell to $21,567 for a population 65 years or older and to $18,726 for a population at higher risk of cardiovascular and gastrointestinal adverse events. CONCLUSION: The model estimates that duloxetine is a moderately cost-effective treatment for CLBP, becoming more cost-effective for populations older than 65 years or at greater risk of cardiovascular and gastrointestinal events. LEVEL OF EVIDENCE: 1.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".