Methods for the analysis of bleeding outcomes in randomized trials of PLT transfusion triggers
Bibliographic record
Abstract
BACKGROUND: A number of methodologic challenges arise in the analysis of bleeding data from clinical trials of PLT transfusion triggers. It is important to understand the assumptions and role of the various methods of analysis to interpret published trials and to design future studies appropriately. STUDY DESIGN AND METHODS: The methods of analysis used for testing the effectiveness and safety of transfusion strategies are reviewed from several recent PLT transfusion trigger trials. The underlying assumptions of these methods are discussed, as well as the clinical interpretations of the resulting summary statistics. Four methods of analysis were applied to data from a large PLT transfusion trigger study to illustrate the differences in the interpretations that can arise from various approaches. RESULTS: PLT transfusion trigger trials of patients with leukemia have based their primary analyses on 1) simple dichotomous classifications of whether or not at least 1 day of clinically important bleeding was experienced; 2) the time to the first day of clinically important bleeding; and 3) the proportion of days at risk with clinically important bleeding. Recurrent event methods provide a robust alternative approach to the analysis of this kind of data and should be considered if interest is in capturing the overall burden of bleeding over time. These four methods differ in the extent to which they utilize information on the number of days with bleeding and the temporal variation in bleeding patterns. Inferences drawn regarding the relative safety and efficacy of different transfusion triggers can vary depending on the method of analysis. CONCLUSION: To rigorously design and analyze future PLT transfusion studies based on bleeding outcomes, it is important to have a clear understanding of the interpretation of the different ways of analyzing bleeding outcomes. The analysis strategy should be selected based on the clinical question being addressed.
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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 | 0.541 | 0.722 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".