Reduced time to efficacy signals in advanced cancer trials using Chauhan weighted trajectory analysis.
Bibliographic record
Abstract
e23028 Background: As Kaplan-Meier (KM) analyzes single unidirectional endpoints, most advanced cancer randomized clinical trials (RCTs) are powered for a 10 endpoint of either progression free survival (PFS) or overall survival (OS). This disregards efficacy information carried by stable disease (SD), partial response (PR), and complete response (CR) that may precede progressive disease (PD) and death. Chauhan Weighted Trajectory Analysis (CWTA), a generalization of KM, permits simultaneous evaluation of multiple rank ordered endpoints. We hypothesized that using CWTA with all efficacy inputs could expedite RCT efficacy signals. Methods: We performed 100-fold simulations of RCTs in advanced cancer and determined the time to first significant efficacy signal (p < 0.05) for KM PFS and OS (logrank test) and CWTA (weighted logrank test). RCTs were stochastically generated at defined sample size (SS) allocated 1:1 to control or intervention and run for 60 months. Rank ordered health statuses were CR = 0, PR = 1, SD = 2, PD = 3, and Death = 4. All patients we assigned SD at time 0 and, each month, were capable of response (PR then CR), maintained SD, or irreversible exacerbation to PD or Death. Event probabilities were modified between groups as defined by a hazard ratio (HR). We modeled a control group CR rate of ~10% and a PR rate of ~50% to reflect first-line advanced cancer RCTs with a dropout rate of 10% over 60 months. At increments of SS and HR, we determined the mean and standard deviation of time-to-efficacy signals. Results: CWTA markedly reduced the time-to-efficacy signals when compared to KM PFS (23% to 67%) and KM OS (43% to 82%) (Table 1). Conclusions: CWTA, by incorporating the entirety of the cancer trajectory including disease response, progression, and death, meaningfully expedites the efficacy signals of cancer treatments compared to KM PFS and OS. We have previously reported CWTA increases trial power and reduces sample size requirements (Chauhan U, Zhao K, Walker J, Mackey JR; 2023. DOI: 10.3390/biomedinformatics3040052). Using CWTA rather than KM reduces trial duration, cost, and numbers of patients needed to evaluate therapies in advanced cancer, while reducing the regulatory risk inherent in powering a trial for a single KM primary endpoint. [Table: see text]
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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 | 0.038 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".