Can administrative data improve the performance of clinical trial economic analyses?
Bibliographic record
Abstract
2 Background: Economic analyses of trials often rely on trial-collected health resource utilization data, which is expensive and may be incompletely recorded. We investigated whether routinely collected health administrative data (RCD) can be utilized to improve trial economic analysis performance. Methods: Health administrative data was probabilistically linked to Ontario patient data from the Canadian Cancer Trials Group CO.17 trial (n = 572), evaluating cetuximab plus best supportive care (n = 75 linked Ontario patients) versus best supportive care alone (n = 73). Completeness of trial data was compared to RCD. Cost-effectiveness in 2007 Canadian dollars was determined using RCD up to trial date of last contact (DOLC), and up to RCD DOLC. Incremental cost effectiveness ratio (ICER) confidence intervals (CI) were determined using bootstrapping with 5000 iterations. Cost-effectiveness acceptability curves were determined. Sensitivity analyses were performed. Results: Among 148 Ontario patients, up to trial DOLC, RCD vital status was concordant in > 96%. 29 deaths occurred after trial DOLC. Up to trial DOLC there were 34 net additional hospitalizations in RCD, and 28 net additional emergency room visits. Using RCD, total cetuximab group costs were $3,023,034, and $1,191,118 for best supportive care alone up to trial DOLC. Cost difference was driven by cetuximab drug costs ($1,531,370). Using RCD, the ICER was $211,128 per life-year gained (90% CI: $101,396, $694,950) when data was limited to trial DOLC, and $164,378 (90% CI: -$138,260, $644,555) using routinely collected data DOLC. ICER estimates were similar to the original economic analysis using trial-collected data ($199,742 (95% CI $125,973, $652,492)). Estimates were robust in sensitivity analysis. Conclusions: Administrative data were more complete than trial-collected utilization data, even under optimal conditions. There was also longer follow-up. We found that cost differences were robust to varying costing assumptions. Our findings demonstrate the potential of administrative data sources to relieve institutions, sponsors and patients from the burden of collecting key utilization information which requires considerable effort and cost.
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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 | 0.596 | 0.869 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".