Pharmacoeconomic analysis of paliperidone palmitate versus olanzapine pamoate for chronic schizophrenia in Norway
Bibliographic record
Abstract
OBJECTIVE: Paliperidone palmitate long-acting injection (PP-LAI) has recently been approved for treatment of chronic schizophrenia. Its cost-effectiveness has not been established. The objective was to compare direct costs and outcomes between PP-LAI and olanzapine pamoate (OLZ-LAI) in treating chronic schizophrenia in Norway from the perspective of the government payer. METHODS: We used a decision analytic model over a 1-year time horizon. Clinical inputs were derived from the literature and an expert panel; costs were taken from standard lists, adjusted to 2010 Norwegian kroner (NOK). Discounting was not done. Main outcomes included average cost per patient treated, hospitalisations, emergency room (ER) visits and quality-adjusted life years (QALYs). The pharmacoeconomic outcome was the incremental cost per QALY. Robustness was examined using one-way sensitivity analyses on critical variables and a 5000-iteration probabilistic Monte Carlo sensitivity analysis with all variables included. RESULTS: PP-LAI generated 0.845 QALY at a cost of 151 336 NOK of which 23% was due to drugs; 25% of patients were hospitalised and another 12% required ER visits. OLZ-LAI cost 174 351 NOK (21% due to drugs); patient outcomes included 0.844 QALY, 27% hospitalisations and 14% ER visits. PP-LAI dominated OLZ-LAI in the base case. The analysis was reasonably robust against variations in drug cost but sensitive to small changes in adherence and hospitalisation rates. Overall, PP-LAI was dominant over OLZ-LAI in 54.5% of simulations. Replacing OLZ-LAI with PP-LAI would be cost saving for the Norwegian healthcare system. CONCLUSION: PP-LAI was cost-effective compared with OLZ-LAI in treating patients with chronic schizophrenia in Norway but sensitive to changes in adherence and hospitalisation rates.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".