A Targeted Review of Worldwide Indirect Treatment Comparison Guidelines and Best Practices
Bibliographic record
Abstract
OBJECTIVES: Controls and governance over the methodology and reporting of indirect treatment comparisons (ITCs) have been introduced to minimize bias and ensure scientific credibility and transparency in healthcare decision making. The objective of this study was to highlight ITC techniques that are key to conducting objective and analytically sound analyses and to ascertain circumstantial suitability of ITCs as a source of comparative evidence for healthcare interventions. METHODS: Ovid MEDLINE was searched from January 2010 through August 2023 to identify publicly available ITC-related documents (ie, guidelines and best practices) in the English language. This was supplemented with hand searches of websites of various international organizations, regulatory agencies, and reimbursement agencies of Europe, North America, and Asia-Pacific. The jurisdiction-specific ITC methodology and reporting recommendations were reviewed. RESULTS: Sixty-eight guidelines from 10 authorities worldwide were included for synthesis. Many of the included guidelines were updated within the last 5 years and commonly cited the absence of direct comparative studies as primary justification for using ITCs. Most jurisdictions favored population-adjusted or anchored ITC techniques opposed to naive comparisons. Recommendations on the reporting and presentation of these ITCs varied across authorities; however, there was some overlap among the key elements. CONCLUSIONS: Given the challenges of conducting head-to-head randomized controlled trials, comparative data from ITCs offer valuable insights into clinical-effectiveness. As such, multiple ITC guidelines have emerged worldwide. According to the most recent versions of the guidelines, the suitability and subsequent acceptability of the ITC technique used depends on the data sources, available evidence, and magnitude of benefit/uncertainty.
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 | 0.029 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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; 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".