Statistical Monitoring of Clinical Trials Using Brownian Bridges
Bibliographic record
Abstract
The Brownian bridge is not yet used widely in the statistical monitoring of clinical trials. In this paper, we investigate properties of the Brownian bridge and formally derive monitoring rules from these results. We will present four related main methods: (1). derivation of group sequential boundaries; (2). calculation of conditional power; (3). a new alpha spending function and (4). repeated confidence intervals, all under a Brownian bridge framework. Simulation results show that the type I error rate is well controlled and power is satisfactory for the group sequential design. We apply the proposed methods to monitor the interim results from the Beta Blocker Heart Attack Trial (BHAT) and a Head and Neck cancer trial with comparisons to the commonly used monitoring tools. Overall, the proposed methods when used together as one framework are more powerful and sensitive to interim positive and negative trends that are clinically meaningful and lead to timely early stopping with potentially more savings on sample sizes, time and costs. These tools are valuable additions to the existing group sequential methods which can be utilized in trial design, routine monitoring, and to answer important questions from data monitoring committees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.085 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".