Sensitivity analyses from the INPULSIS™ trials of nintedanib
Bibliographic record
Abstract
Background: Nintedanib, an intracellular inhibitor of tyrosine kinases, is in development for the treatment of idiopathic pulmonary fibrosis (IPF). The INPULSIS™ trials were two replicate, 52-week, randomized, double-blind, placebo-controlled Phase III trials that investigated the efficacy and safety of nintedanib 150 mg twice daily in 1066 patients with IPF. The primary endpoint was the annual rate of decline in forced vital capacity (FVC). Key secondary endpoints were time to first acute exacerbation over 52 weeks and change from baseline in St. George's Respiratory Questionnaire (SGRQ) total score over 52 weeks. Aim: Sensitivity analyses were conducted to test the robustness of the results of the primary and key secondary endpoints in the INPULSIS™ trials. Methods: Pre-specified sensitivity analyses tested model assumptions and sensitivity to data handling, including handling of missing data, in each trial. Results: In both trials, sensitivity analyses were all consistent with the primary analysis of the annual rate of decline in FVC, confirming superiority of nintedanib versus placebo. Sensitivity analyses for the key secondary endpoints were consistent with the primary analyses in each trial. All treatment effect estimates were very close to estimates of the primary analyses, confirming a significantly reduced risk of acute exacerbation and a significantly smaller increase in SGRQ total score (indicating less deterioration in health-related quality of life) in the nintedanib group versus placebo in INPULSIS™-2 and no significant difference between groups in INPULSIS™-1. Conclusion: In the INPULSIS™ trials, the robustness of the primary and key secondary endpoint results was supported by sensitivity analyses.
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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 | 0.220 | 0.338 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.030 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".