Transporting: What Is It and How Do You Do It?
Bibliographic record
Abstract
Purpose of Review: Transportability, one of the twin faces of external validity (alongside generalizability), refers to the ability to use effect estimates in a study population to understand effects in a different population. In this review, we aimed to provide an overview of ongoing methodological developments in the field of transportability and provide a tutorial walking through key steps in the transportability process. Recent Findings: We cover recent work done to distinguish the concept of transportability from generalizability (or external validity more broadly), define core conditions necessary for transporting treatment effects to a different target population, outline approaches to identify sufficient adjustment sets, and design estimators to estimate transported treatment effects. We then illustrate the application of these methods through a case study comparing the effects of two adjuvant chemotherapies for breast cancer in patients within the National Cancer Database, a large national cancer registry, using effect estimates transported from a randomized controlled trial. Summary: While external validity, generalizability, and transportability have long been recognized as important elements of epidemiology, they have historically been treated interchangeably and discussed qualitatively in discussion sections of manuscripts. Over the past two decades, however, major strides have been made to formally define these concepts and introduce analytic methods for them that are valid under well-defined conditions.
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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 | 0.114 | 0.401 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".