Statistical Considerations in Clinical Trial Design of Immunotherapeutic Cancer Agents
Bibliographic record
Abstract
The classical model for identification and clinical development of anticancer agents was based on small molecules, which were often quite toxic. Early studies in small groups of patients would seek to identify a maximum tolerated dose and major dose-limiting toxicities. Tumor response (shrinkage) would be assessed after a minimum number of doses in phase II testing. The decision to take the drug into the randomized phase III clinical setting was usually based on the proportion and duration of objective tumor responses, along with overall survival compared with historical controls. Immune-oncologics that are designed to fight cancer by direct CD8(+) T-cell priming and activation or by blocking a negative regulatory molecule have a number of sharp distinctions from cytotoxic drugs. These include cytoreductive effects that may be very different in timing of onset from traditional chemotherapy and the potential for inducing long-term durable remissions even in heavily pretreated patients with metastatic disease. In this paper we review the different classes of immune-oncologic drugs in clinical development with particular attention to the biostatistical challenges associated with evaluating efficacy in clinical trials. Confronting these issues upfront is particularly important given the rapidly expanding number of clinical trials with both monotherapy and combination trials in immunooncology.
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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 | 0.599 | 0.710 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".