Global Transportability of Clinical Trial Outcomes to Real-World Lung Cancer Populations A case Study using Lung-MAP S1400I
Bibliographic record
Abstract
Abstract Importance The relevance of randomized clinical trials (RCTs) outcomes to real-world settings – especially across countries – is sometimes limited by their restrictive eligibility criteria and variations in standards of care compared to routine clinical practice. Objective To assess the transportability of findings from the RCT Lung-MAP S1400I to real-world populations in the United States (US), Germany and France. Design This empirical validation study used patient-level data from the RCT Lung-MAP S1400I to build a transportability model to adjust for differences in patient characteristics from real-world target patient populations. Two sets of adjustments were performed – one limited to the set of measured clinical variables, and the second additionally including external information drawn from published literature and substantive knowledge on patient subgroups excluded from the trial. The latter enabled transportability to a significantly more diverse and representative real-world patient population by relaxing the stringent exclusion criteria used in Lung-MAP S1400I. For benchmarking, we compared how well the transportability analysis approximated observed overall survival in the respective real-world cohorts. Setting Observational study. Participants Eligible individuals diagnosed with advanced or metastatic NSCLC and previously treated with systemic therapy. Intervention/exposure Nivolumab monotherapy. Main outcome measures Overall survival. Results Sample size for the nivolumab arm in Lung-MAP S1400I was 127 and ranged from 133 to 1051 for the various real-world cohorts included. Patients with ECOG scores of 2+, index cancer stage ALK / EGFR mutations, presence of comorbidities and prior exposure to immunotherapy/targeted therapies were excluded from Lung-MAP S1400I, were but were eligible to receive nivolumab monotherapy in real-world care. Adjusting for measured clinical differences improved alignment of patient outcomes in the RCT and the real-world cohorts. However, only when variables related to excluded patient groups were also addressed did the results fully satisfy control conditions, yielding the closest approximation to real-world survival in the US, Germany, and France (mean discrepancy: 0.27 months over ∼30 months). Conclusions Overall survival in a more diverse real-world patient population could be extrapolated using data from the Lung-MAP S1400I trial when complemented with external information about excluded patient groups.
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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 | 0.348 | 0.589 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".