Patient-reported outcomes and target effect sizes in pragmatic randomized trials in ClinicalTrials.gov: A cross-sectional analysis
Notice bibliographique
Résumé
BACKGROUND: Use of patient-reported outcomes (PROs) and patient and public engagement are critical ingredients of pragmatic trials, which are intended to be patient centered. Engagement of patients and members of the public in selecting the primary trial outcome and determining the target difference can better ensure that the trial is designed to inform the decisions of those who ultimately stand to benefit. However, to the best of our knowledge, the use and reporting of PROs and patient and public engagement in pragmatic trials have not been described. The objectives of this study were to review a sample of pragmatic trials to describe (1) the prevalence of reporting patient and public engagement; (2) the prevalence and types of PROs used; (3) how its use varies across trial characteristics; and (4) how sample sizes and target differences are determined for trials with primary PROs. METHODS AND FINDINGS: This was a methodological review of primary reports of pragmatic trials. We used a published electronic search filter in MEDLINE to identify pragmatic trials, published in English between January 1, 2014 and April 3, 2019; we identified the subset that were registered in ClinicalTrials.gov and explicitly labeled as pragmatic. Trial descriptors were downloaded from ClinicalTrials.gov; information about PROs and sample size calculations were extracted from the manuscript. Chi-squared, Cochran-Armitage, and Wilcoxon rank sum tests were used to examine associations between trial characteristics and use of PROs. Among 4,337 identified primary trial reports, 1,988 were registered in CT.gov, of which 415 were explicitly labeled as pragmatic. Use of patient and public engagement was identified in 39 (9.4%). PROs were measured in 235 (56.6%): 144 (34.7%) used PROs as primary outcomes and 91 (21.9%) as only secondary outcomes. Primary PROs were symptoms (64; 44%), health behaviors (36; 25.0%), quality of life (17; 11.8%), functional status (16; 11.1%), and patient experience (10; 6.9%). Trial characteristics with lower prevalence of use of PROs included being conducted exclusively in children or adults over age 65 years, cluster randomization, recruitment in low- and middle-income countries, and primary purpose of prevention; trials conducted in Europe had the highest prevalence of PROs. For the 144 trials with a primary PRO, 117 (81.3%) reported a sample size calculation for that outcome; of these, 71 (60.7%) justified the choice of target difference, most commonly, using estimates from pilot studies (31; 26.5%), standardized effect sizes (20; 17.1%), or evidence reviews (16; 13.7%); patient or stakeholder opinions were used to justify the target difference in 8 (6.8%). Limitations of this study are the need for trials to be registered in ClinicalTrials.gov, which may have reduced generalizability, and extracting information only from the primary trial report. CONCLUSIONS: In this study, we observed that pragmatic trials rarely report patient and public engagement and do not commonly use PROs as primary outcomes. When provided, target differences are often not justified and rarely informed by patients and stakeholders. Research funders, scientific journals, and institutions should support trialists to incorporate patient engagement to fulfill the mandate of pragmatic trials to be patient centered.
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 | 0,561 | 0,757 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,020 |
| Bibliométrie | 0,019 | 0,023 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,005 | 0,009 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex 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 ».