Statistical analysis of cost–effectiveness data from randomized clinical trials
Bibliographic record
Abstract
Since the mid-1990s, motivated by the availability of patient-level cost data in randomized clinical trials, there has been rapid development in the statistical methods for analyzing cost-effectiveness data. Initial efforts concentrated on inference about the incremental cost-effectiveness ratio (ICER), but due to difficulties associated with ratio statistics, interest has settled more recently on incremental net benefit (INB). Regardless of the approach, five parameters need to be estimated: the between-treatment arm differences in mean effectiveness and mean cost, and the corresponding variances and covariance. With the estimates of these parameters, the analyst can plot the cost-effectiveness acceptability curve and estimate the ICER and the INB, and calculate confidence limits for both. A review of these methods is given. The particular statistical procedure used for estimating the five parameters depends on whether or not censoring is present; whether or not covariates are adjusted for; whether or not random effects, such as country, are adjusted for; and the assumptions regarding the distribution for cost. A review of the statistical procedures, particular to each combination of these conditions, is given, where they exist.
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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 | 0.404 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; both teacher heads agree on what is shown here.
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".