Assessing long-term survival improvement of immune-checkpoint inhibitors (ICI) anticancer agents approved by the Food and Drug Administration: A meta-analysis of randomized controlled trials (RCTs).
Bibliographic record
Abstract
e15062 Background: Conventional anticancer drugs have been developed to improve overall survival (OS) and/or progression-free survival (PFS) in the palliative intent setting. However, long-term survival is often limited by acquired biologic resistance. In contrast to conventional anticancer drugs, ICIs putatively preserve long-term survival in a small subset of patients. However, in the absence of a gold-standard definition, long-term survival improvement has not been adequately quantified. Long-term survival rate difference has been proposed as a method of quantifying long-term survival. However, there a paucity of literature demonstrating statistical improvements in survival rate. Therefore, the objective of the study was to quantify the improvement in long-term survival rate in OS and PFS improvement in ICIs compared with non-ICIs. Methods: The FDA Hematology/Oncology Approvals & Safety Notification web page was searched for RCTs cited in oncology drug approvals between 01-2011 and 06-2019. The most recent OS and PFS updates were used when available. OS and PFS curves were digitized to reconstruct pseudo individual patient data as per established methods. Survival rate improvement at 1, 2 and 3 years were calculated as differences between the experimental arm and control arm. Survival rate improvements across RCTs at 1, 2 and 3 years were pooled using a random-effects model and the results were compared between ICI and non-ICIs. Subgroup analyses were conducted for melanoma and non-small cell lung cancer. Results: We identified 130 RCTs (22 ICIs, 108 non-ICIs) for analysis. The absolute OS rate improvement was 9.5% in ICIs and 4.9% in non-ICIs at 1-year (difference = 4.6%, p < 0.001), 10.3% in ICIs and 4.9% in non-ICIs at 2-years (difference = 5.3%, p < 0.001), and 10.2% in ICIs and 5.1% in non-ICIs at 3-years (difference = 4.6% p 0.006). The absolute PFS rate improvement was 10.3% in ICIs and 17.7% in non-ICIs at 1-year (difference = -7.3% p < 0.001), and 8.9% in ICIs and 15.5% in non-ICIs at 2-years (difference = -6.6% p < 0.001). Conclusions: Long-term OS survival rate improvements, while statistically significant were only modestly higher in ICIs compared to non-ICIs. PFS survival rate differences were statistically lower in ICIs compared to non-ICIs. While ICIs represent an important shift towards long-term survival, greater emphasis should be placed on rigorously evaluating long-term survival rate improvement and reporting of future novel therapies.
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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 | 0.043 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.054 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".