A systematic review and development of a classification framework for factors associated with missing patient-reported outcome data
Notice bibliographique
Résumé
BACKGROUND/AIMS: Missing patient-reported outcome data can lead to biased results, to loss of power to detect between-treatment differences, and to research waste. Awareness of factors may help researchers reduce missing patient-reported outcome data through study design and trial processes. The aim was to construct a Classification Framework of factors associated with missing patient-reported outcome data in the context of comparative studies. The first step in this process was informed by a systematic review. METHODS: Two databases (MEDLINE and CINAHL) were searched from inception to March 2015 for English articles. Inclusion criteria were (a) relevant to patient-reported outcomes, (b) discussed missing data or compliance in prospective medical studies, and (c) examined predictors or causes of missing data, including reasons identified in actual trial datasets and reported on cover sheets. Two reviewers independently screened titles and abstracts. Discrepancies were discussed with the research team prior to finalizing the list of eligible papers. In completing the systematic review, four particular challenges to synthesizing the extracted information were identified. To address these challenges, operational principles were established by consensus to guide the development of the Classification Framework. RESULTS: A total of 6027 records were screened. In all, 100 papers were eligible and included in the review. Of these, 57% focused on cancer, 23% did not specify disease, and 20% reported for patients with a variety of non-cancer conditions. In total, 40% of the papers offered a descriptive analysis of possible factors associated with missing data, but some papers used other methods. In total, 663 excerpts of text (units), each describing a factor associated with missing patient-reported outcome data, were extracted verbatim. Redundant units were identified and sequestered. Similar units were grouped, and an iterative process of consensus among the investigators was used to reduce these units to a list of factors that met the guiding principles. The list was organized on a framework, using an iterative consensus-based process. The resultant Classification Framework is a summary of the factors associated with missing patient-reported outcome data described in the literature. It consists of 5 components (instrument, participant, centre, staff, and study) and 46 categories, each with one or more sub-categories or examples. CONCLUSION: A systematic review of the literature revealed 46 unique categories of factors associated with missing patient-reported outcome data, organized into 5 main component groups. The Classification Framework may assist researchers to improve the design of new randomized clinical trials and to implement procedures to reduce missing patient-reported outcome data. Further research using the Classification Framework to inform quantitative analyses of missing patient-reported outcome data in existing clinical trials and to inform qualitative inquiry of research staff is planned.
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 | 0,387 | 0,629 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,004 |
| Méta-épidémiologie (sens large) | 0,015 | 0,023 |
| Bibliométrie | 0,079 | 0,052 |
| Études des sciences et des technologies | 0,006 | 0,007 |
| Communication savante | 0,012 | 0,017 |
| Science ouverte | 0,008 | 0,011 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».