Group sequential analysis of marked point processes: Plasma donation trials
Bibliographic record
Abstract
Plasma donation plays a critical role in modern medicine by providing lifesaving treatments for patients with a wide range of conditions like bleeding disorders, immune deficiencies, and infections. Evaluation of devices used to collect blood plasma from donors is essential to ensure donor safety. We consider the design of plasma donation trials when the goal is to assess the safety of a new device on the response to transfusions compared to the standard device. A unique feature is that the number of donations per donor varies substantially so some individuals contribute more information and others less. The sample size formula is derived to ensure power requirements are met when analyses are based on generalized estimating equations and robust variance estimation. Strategies for interim monitoring based on group sequential designs using alpha spending functions are developed based on a robust covariance matrix for estimates of treatment effect over successive analyses. The design of a plasma donation study is illustrated where the focus is on assessing the safety of a new device with serious hypotensive adverse events as the primary outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.260 | 0.962 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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; both teacher heads 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".