Reply to McShane
Notice bibliographique
Résumé
To the Editor—We are pleased to see that our recent Perspective entitled, “An aspiration to radically shorten phase 3 tuberculosis vaccine trials” [1] was accompanied by a commentary by experienced tuberculosis vaccine researcher Professor Helen McShane [2]. We hope that these pieces will stimulate debate about this important topic. As part of this, we would like to address some of the statements made by Professor McShane. First, it is argued that key aspects of our suggested approach would be unlikely to be acceptable in today's ethical and regulatory environment. Our view is that the opinion of regulators should not be presupposed and engagement of them in these discussions is crucial, so more creative and efficient trial designs might be fully considered in vaccine development. Second, at the heart of the issues raised are the safety data from the trial and case ascertainment. With respect to the safety data set, it does not make epidemiological sense that the sample size required for efficacy must be the same, or even similar, as that for safety. Our short-duration vaccine trial design proposes to enroll a larger number of participants to measure efficacy. However, for a disease with relatively low incidence of cases over follow-up, such as tuberculosis, whether all participants need to be subject to all safety evaluations for regulatory purposes is especially important to consider. We propose that safety follow-up for regulatory purposes should have its own independent sample size requirements and that this may well result in a smaller number of participants needing stringent safety assessment compared to efficacy evaluation. We note that such an approach would not be new. For example, only 58% of participants in a phase 3 trial of a herpes zoster subunit vaccine underwent stringent safety follow-up [3]. With respect to case ascertainment, it is important not to conflate stringency of end point ascertainment with completeness. Stringency of end point ascertainment is about specificity, while completeness is about sensitivity. Our key point is that in a vaccine trial specificity should be maximized to prevent bias in the estimation of vaccine efficacy, but that sensitivity can be less than 100% because it will only reduce statistical power without affecting the validity of the efficacy estimate. The decision relates to efficiency, not vaccine efficacy estimation. As such, the effort needed to reach 100% sensitivity should be balanced against the effort needed to enroll the number of trial participants to make up for the loss in power when sensitivity to detect a case is lower than that. Finally, we want to thank Professor McShane and the journal for stimulating discussions on strategies to come up with a more effective tuberculosis vaccine in the shortest amount of time. We hope that fellow researchers, regulators, and the other relevant stakeholders engage to find creative alternatives to the traditionally slow and extremely expensive pathway for tuberculosis vaccine development. Financial support. No financial support was received for this work.
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 | 0,006 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,011 | 0,005 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,114 | 0,083 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,011 |
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 ».