Cost‐effectiveness analysis of a multinational RCT with a binary measure of effectiveness and an interacting covariate
Bibliographic record
Abstract
In a recent multinational randomized clinical trial, 1356 patients from 14 countries were randomized between two arms. The primary measure of effectiveness was 30-day survival. Health care utilization was collected on all patients and was combined with a single country's price weights to provide patient-level cost data. The purpose of this paper is to report the results of the cost-effectiveness analysis for the country that provided the cost weights, so as to provide a case study for illustrating recently proposed methodologies that account for skewed cost data, the between-country variation in treatment effects, possible interactions between treatment and baseline covariates, and the difficulty of estimated adjusted risk differences. A hierarchal model is used to account for the two sources of variation (between country and between patients, within a country). The model, which uses gamma distributions for cost data and recent methods for estimating adjusted risk differences, provides overall and country-specific estimates of treatment effects. Model estimation is facilitated by Markov chain Monte Carlo methods using the WinBUGS software. In addition, the theory of expected value of information is used to determine if the data provided by the trial are sufficient for decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".