Use of transportability methods for real-world evidence generation: a review of current applications
Bibliographic record
Abstract
Aim: To evaluate how transportability methods are currently used for real-world evidence (RWE) generation to inform good practices and support adoption and acceptance of these methods in the RWE context. Methods: We conducted a targeted literature review to identify studies that transported an effect estimate of the clinical effectiveness or safety of a biomedical exposure to a target real-world population. Records were identified from PubMed-indexed articles published any time before 25 July 2023 (inclusive). Two reviewers screened abstracts/titles and reviewed the full text of candidate studies to identify the final set of articles. Data on the therapeutic area, exposure(s), outcome(s), original and target populations and details of the transportability analysis (e.g., analytic method used, estimate transported, stated assumptions) were abstracted from each article. Results: Of 458 unique records identified, six were retained in the final review. Articles were published during 2021–2023, focused on the US/Canada context, and covered a range of therapeutic areas. Four studies transported an RCT effect estimate, while two transported effect estimates derived from real-world data. Almost all articles used weighting methods to transport estimates. Two studies discussed all transportability assumptions, and one evaluated the likelihood of meeting all assumptions and the impact of potential violations. Conclusion: The use of transportability methods for RWE generation is an emerging and promising area of research to address evidence gaps in settings with limited data and infrastructure. More transparent and rigorous reporting of methods, assumptions and limitations may increase the use and acceptability of transportability for producing robust evidence on treatment effectiveness and safety.
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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 | 0.439 | 0.759 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.051 | 0.041 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".