Choosing the optimal endpoint(s) for a clinical trial on transfusion
Bibliographic record
Abstract
The practice of clinical medicine is based, for the most part, on the results of clinical trials. Many issues, however, can make the interpretation of any trial's results difficult. The validity of a clinical trial depends on many factors of design and implementation, for example, randomization, blinding, and use of an appropriate control. Perhaps the most overlooked critical factor impacting the validity and clinical relevance of a clinical trial is the choice of the primary endpoint. The primary outcome to be studied must reflect the hypothesis to be tested, with the sample size calculated to have adequate power to detect a difference of meaningful magnitude while simultaneously minimizing the chances of detecting a difference by chance.1 Thus, the selected endpoint must be able to directly answer the question posed, be quantified with an appropriate trial design of an obtainable population sized to likely provide the answer, and be clinically relevant. Unfortunately, this is not always possible. One or more of these factors may dictate that something other than the primary, direct measure be selected as an endpoint, in which case a surrogate endpoint is chosen. Other, less objective issues may impact the selection of a trial endpoint. Coinvestigators may differ in their opinions regarding the best or most important outcome. Ease of quantification or cost of alternative trial designs based on differing endpoints may influence selection. In some cases, a sponsor's imperatives, or the desires or perceived desires of a regulatory authority, may dictate the selection of the endpoint of a clinical trial. In some areas of medicine, the choice of trial design is quite straightforward. Regrettably, in the case of transfusion medicine, this is not always the case. Outcomes of transfusion trials may range from most immediate (bleeding, hematologic variables, requirements for red cells or hemostatic blood products) to more remote outcomes (morbidity and/or major organ dysfunction, length of hospital stay, mortality). Although very few randomized, controlled studies support the use of blood products in an objective fashion,2 transfusion practice has been the object of numerous recommendations. Thus, despite the paucity of evidence, the implementation of an appropriate control group may not be easy. Transfusion trials are notoriously difficult to blind, and bias is hard to avoid. Finally, the consequences of moderate anemia (e.g., myocardial ischemia), the benefits of transfusions (e.g., tissue oxygenation), and their adverse effects (e.g., immunosuppression) have not been demonstrated clearly in the clinical setting. As a result, the relationship between outcomes, whether beneficial or harmful, subsequent to transfusion (e.g., length of stay, morbidity, and mortality) and transfusions themselves, remains to be established. In this supplement to TRANSFUSION, a group of experts discuss endpoints of clinical trials that are of interest and concern in the field of transfusion, more specifically in the areas of hemostasis and/or bleeding, trauma, and blood-sparing strategies. Dr Silverman and colleagues of the US FDA add the thoughts of “a regulator’s” viewpoint. We hope that their views will be of use to all those interested in transfusion medicine, whether they be clinicians or actively engaged in clinical research.
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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 | 0.133 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".