Clinician-trialist rounds: 6. Testing for blindness at the end of your trial is a mug's game
Notice bibliographique
Résumé
Today’s definition: Mug’s game: a useless or illadvised venture carried out by a gullible person. Today’s case: We’d just completed the first-ever RCT showing that aspirin (but not sulfinpyrazone) reduced the risk of stroke and death among patients with transient ischemic attacks.* We were elated with our primary results and already dreaming of a lead article in the New England Journal of Medicine. There were just a few odds and ends to attend to, and one of them was analyzing an end-of-study questionnaire we’d given to our collaborating neurologists to confirm that our efforts to keep them blind had been successful. Our trial had employed a ‘double-dummy’ factorial design in which patients were randomized to both active drugs, to active aspirin and placebo sulfinpyrazone, to placebo aspirin and active sulfinpyrazone, or to both placebos. Consequently, when we asked our neurologists which regimen they thought each of their patients had received, they would have guessed correctly for 25% of them on the basis of chance alone. Any big increase in this rate of correct responses would be worrisome, and a statistically significant difference would suggest that our attempts to blind them had failed. ‘I felt the bullet enter my heart’ [1] when our co-PI statistician tracked me down on the ward to tell me that our clinicians’ correct guesses were, indeed, statistically significantly different from 25%. Had our triumphant lead article just been reduced to an apologetic Letter to the Editor? And why did my co-PI have a big grin on his face? I have lots of textbooks on how to do ‘doubleblind’y RCTs [2], and (except for one recent revision) all of them recommend end-of-study tests for blindness on both patients and providers. They go on to warn that greater-than-chance correct guesses raise real concerns about whether blinding was successful, and that when this occurs, trial reports should admit these failures and temper their conclusions accordingly. Moreover, reviews of published RCTs have found that these textbook recommendations are rarely reported. Isabelle Boutron led a review of 90 trials obtained from several bibliographic databases and concluded: ‘Methods of assessing the success of blinding, analysis and reporting the results were inconsistent and questionable.’[3]. Testing for blinding was reported in only 8% of the random sample of 199 general medicine and psychiatry RCTs published in 1998–2001 assessed by Dean Fergusson and his colleagues [4] and in only 2% of a random sample of 1599 RCTs published in 2001 and drawn from the Cochrane Central Register of Controlled Trials by Asbjorn Hrobjartsson’s team [5]. Gloomier still (at least in these authors’ eyes), in the rare instances in which tests were carried out, blinding was judged to have been successful only one-third to one-half of the time. As with cointervention in our previous Round, understanding the measurement of blinding requires the synthesis of methodological and clinical competence. The key question in this Round is: What are you really measuring when you measure ‘blindness’ at the end of your trial? And the quick
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,727 | 0,871 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,012 | 0,011 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,003 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,002 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».