Improving efficiency in the stepped-wedge trial design via Bayesian modeling with an informative prior for the time effects
Notice bibliographique
Résumé
BACKGROUND: In a cross-sectional stepped-wedge cluster randomized trial comparing usual care to a new intervention, treatment allocation and time are correlated by design because participants enrolled early in the trial predominantly receive usual care while those enrolled late in the trial predominantly receive the new intervention. Current guidelines recommend adjustment for time effects when analyzing stepped-wedge cluster randomized trials to remove the confounding bias induced by this correlation. However, adjustment for time effects impacts study power. Within the Frequentist framework, adopting a sample size calculation that includes time effects would ensure the trial having adequate power regardless of the magnitude of the effect of time on the outcome. But if in fact time effects were negligible, this would overestimate the required sample size and could lead to the trial being deemed infeasible due to cost or unavailability of the required numbers of clusters or participants. In this study, we explore the use of prior information on time effects to potentially reduce the required sample size of the trial. METHODS: We applied a Bayesian approach to incorporate the prior information on the time effects into cluster-level statistical models (for continuous, binary, or count outcomes) for the stepped-wedge cluster randomized trial. We conducted simulations to illustrate how the bias in the intervention effect estimate and the trial power vary as a function of the prior precision and the mis-specification of the prior means of the time effects in an example scenario. RESULTS: When a nearly flat prior for the time effects was used, the power or sample size calculated using the Bayesian approach matched the result obtained using the Frequentist approach with time effects included. When a highly precise prior for the time effects (with accurately specified prior means) was used, the Bayesian result matched the Frequentist result obtained with time effects excluded. When the prior means of the time effects were nearly correctly specified, including this information improved the efficiency of the trial with little bias introduced into the intervention effect estimate. When the prior means of the time effects were greatly mis-specified and a precise prior was used, this bias was substantial. CONCLUSION: Including prior information on time effects using a Bayesian approach may substantially reduce the required sample size. When the prior can be justified, results from applying this approach could support the conduct of a trial, which would be deemed infeasible if based on the larger sample size obtained using a Frequentist calculation. Caution is warranted as biased intervention effect estimates may arise when the prior distribution for the time effects is concentrated far from their true values.
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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 | 0,166 | 0,286 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,005 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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; 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 ».