Assessment of Generalizability Methods to Extend Phase III Clinical Trial Effect Estimates in the Non-Squamous Metastatic NSCLC Population
Bibliographic record
Abstract
Generalizability methods may be used to estimate the population average treatment effect (PATE) of clinical trial therapeutics. However, few studies have applied those methods for oncology. The current work compares findings from three generalizability methods when estimating the PATE of the IMpower150 study (sponsored by F. Hoffmann–La Roche/Genentech) in the United States metastatic non-small-cell lung cancer target population. Inverse Odds of Trial Participation Weighting (IOPW), Parametric Outcome Model Based, and Nonparametric Outcome Model Based generalizability methods were compared using a resampling strategy. Overall, the methods produced significantly different PATE estimates, with the IOPW estimates differing the most from the IMpower150 estimates. Our findings highlight considerations for applying generalizability methods in oncology and the need for improved data sharing efforts across industry. We anticipate our work to be a starting point for development of improved methodology aimed at incorporating real-world data to assess the population level impact of novel therapeutics.
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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 | 0.472 | 0.734 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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, 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".