Validation of an economic model of paliperidone palmitate for chronic schizophrenia
Bibliographic record
Abstract
OBJECTIVE: Model validation is important, but seldom applied in chronic schizophrenia. Validation consists of verifying the model itself for face validity (i.e., structure and inputs), cross-validation with other models assessing the same issue, and comparison with real-life outcomes. The primary purpose was to cross-validate a recent pharmacoeconomic model comparing long-acting injectable (LAI) antipsychotics for treating chronic schizophrenia in Sweden. The secondary purpose was to provide external validation. METHODS: The model of interest was a decision tree analysis with a 1-year time horizon with costs in 2011 Swedish kroner. Drugs analyzed included paliperidone palmitate (PP-LAI), olanzapine pamoate (OLZ-LAI), risperidone (RIS-LAI), haloperidol (HAL-LAI), and oral olanzapine (oral-OLZ). Embase and Medline were searched from 1990-2012 for models examining LAIs. Articles were retrieved, with data extracted for all drugs compared including: expected costs, rates of hospitalization, proportion of time not in relapse, and associated QALYs. Outcomes from the model of interest were compared with those from other articles; costs were projected to 2012 using the consumer price index. RESULTS: Twenty-six studies were used for validation; 14 of them provided evidence for cross-validation, 13 for external validation, and four for cost. In cross-validation, cost estimates varied -1.8% (range: -12.4-20.1%), hospitalizations 5.2% (-12.1-3.1%), stable disease 2.5% (-5.6-1.5%), QALYs 9.0% (4.3% after removing outliers). All estimates of clinical outcomes were within 15%. In external validation, hospitalization rates varied by 6.3% (-0.7-11.3%). The research was limited by data availability and validity of the original results. CONCLUSION: Other models validated the outputs of our model very well.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".