The influence of intervention fidelity on treatment effect estimates in clinical trials of complex interventions: a metaepidemiological study
Notice bibliographique
Résumé
BACKGROUND AND OBJECTIVE: Randomized clinical trials (RCTs) provide the most reliable estimates of treatment effectiveness for therapeutic interventions. However, flaws in their design and conduct may bias treatment effect estimates, leading to overestimation or underestimation of the true intervention effect. This is especially relevant for complex interventions, such as those in rehabilitation, which are multifaceted and tailored for individual patients or providers, leading to variations in delivery and treatment effects. To assess whether poor intervention fidelity, the faithfulness of the intervention delivered in an RCT to what was intended in the trial protocol, influences (biases) estimates of treatment effects derived from meta-analysis of rehabilitation RCTs. METHODS: In this metaepidemiological study of 19 meta-analyses and 204 RCTs published between 2010 and 2020, we evaluated the difference in intervention effects between RCTs in which intervention fidelity was monitored and those in which it was absent. We also conducted random-effects metaregression to measure associations between intervention fidelity, risk of bias, study sample size, and treatment effect estimates. RESULTS: There was a linear relationship between fidelity and treatment effect sizes across RCTs, even after adjusting for risk of bias and study sample size. Higher degrees of fidelity were associated with smaller but more precise treatment effect estimates (d = -0.23 95% CI: -0.38, -0.74). Lower or absent fidelity was associated with larger, less precise estimates. Adjusting for fidelity reduced pooled treatment effect estimates in 4 meta-analyses from moderate to small or from small to no negligible or no effect, highlighting how poor fidelity can bias meta-analyses' results. CONCLUSION: Poor or absent intervention fidelity in RCTs may lead to overestimation of observed treatment effects, skewing the conclusions from individuals studies and systematic reviews with meta-analyses when pooled. Caution is needed when interpreting the results of complex intervention RCTs when fidelity is not monitored or is monitored but not reported. PLAIN LANGUAGE SUMMARY: Patients, the public, and health-care providers rely on clinical trials for information about how effective treatments are when making decisions about health care. However, the way that clinical trials are conducted may alter the evidence that clinical trials provide about how effective interventions truly are. In this study, we investigated whether how closely health-care providers monitor how they deliver rehabilitation treatments to patients in clinical studies, and how closely those treatments match the treatment that the researchers had planned, influences the results of those studies. We found that when researchers or health-care providers don't closely monitor how they deliver treatments during a study, those studies may provide exaggerated estimates of the effectiveness of the treatments studies. This is important, because it may mean that some health-care providers and patients may opt for treatments that are less effective than they appeared in clinical studies, or may overlook treatments that are more effective than they appeared in other studies.
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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 | 0,383 | 0,596 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,004 |
| Méta-épidémiologie (sens large) | 0,019 | 0,075 |
| Bibliométrie | 0,013 | 0,012 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,010 | 0,008 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».