An Evaluation of Linked Administrative Data for Cancer Clinical Trial Economic Analysis
Bibliographic record
Abstract
IntroductionEconomic analyses of well-conducted clinical trials are critical to rational health policy that informs value-based decision making. Trial economic analyses, such as cost-effectiveness analysis, often rely on trial-collected data, which are burdensome and expensive to collect. In contrast, administrative databases systematically collect health system encounters and are available at low cost for nearly all patients with cancer in Ontario, Canada, and many other jurisdictions. Objectives and ApproachWe investigated whether administrative data could improve the performance of trial economic analysis. Health administrative data were probabilistically linked to 148 Ontario patients from the Canadian Cancer Trials Group CO.17 trial (n=572), which evaluated cetuximab plus best supportive care (n=75) versus best supportive care alone (n=73) in previously treated metastatic colorectal cancer. Trial-collected resource utilization data and vital status were compared with administrative data. Cost-effectiveness in 2007 Canadian dollars according to administrative data was determined with bootstrap incremental cost-effectiveness ratio (ICER) confidence intervals (CIs). ResultsUp to trial date of last contact, administrative data vital status was concordant in >96%. Twenty-nine subsequent deaths occurred. Up to trial last contact, there were 50 net additional hospitalizations and 33 net additional emergency department visits in administrative data. Total costs were $3,023,034 for the cetuximab group and $1,191,118 for the control group up to trial last contact. ICER was $211,128 per life-year gained (90% CI: $101,396 to $694,950) up to trial last contact and $164,378 (90% CI: -$138,260 to $644,555) up to administrative data last contact. ICER estimates were similar to analyses using trial-collected data. Conclusion/ImplicationsAdministrative data were more complete than trial data for hospital encounters, a key cost driver in economic analysis and there was longer follow-up. This study demonstrates the potential of administrative data to relieve the burden of collecting key data in cancer trials, which represents considerable effort and expense.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.351 | 0.598 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".