Effects of biomarker diagnostic accuracy on biomarker-guided phase 2 trials
Bibliographic record
Abstract
Recent advancements in genomics have attracted attention towards biomarker-guided trials. These trials aim to identify therapies that target diseases based on their genetic profile, and are especially common in cancer research. Careful incorporation of biomarkers in phase II studies is critical to the selection of candidates for further phase III investigation. This short communication focuses on problems of biomarker test accuracy in biomarker-guided trials. We assessed how diagnostic accuracy of biomarker tests affects type I error rate, statistical power, and sample size requirements of single-arm biomarker-guided trials. In particular, we report how false positive rates (FPRs) of biomarker tests reduce statistical power and type I error for Simon's two-stage design, and the degree of sample size correction required to achieve pre-specified power and type I error with varying FPRs. This was done using a case study based on a previous biomarker-guided single-arm trial that was designed with an assumed tumor response rate of 10% under the null hypothesis and 40% for the alternative hypothesis for the mutant group for 5% type I error and 90% power. With varying FPRs of biomarker tests, we considered two scenarios in which the response rate for the wild-type group was assumed to be lower than the response rate for the mutant group at 5% and 10%. We also developed a simple open-source online trial planner for future investigators to use for their biomarker-guided phase II trials (https://mtek.shinyapps.io/Biomarker_Trial_Planner/).
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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 | 0.419 | 0.667 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".