Choosing the optimal endpoint(s) for a clinical trial on transfusion
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,133 | 0,165 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,005 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».