Reconciling randomized trial evidence on proximal versus distal outcomes, with application to trials of influenza vaccination for healthcare workers.
cluster-randomized trials
influenza
misclassification
probabilistic sensitivity analysis
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
30 09 2019
30 09 2019
Historique:
received:
04
03
2019
revised:
30
05
2019
accepted:
03
06
2019
pubmed:
19
7
2019
medline:
16
1
2021
entrez:
19
7
2019
Statut:
ppublish
Résumé
When synthesizing the body of evidence concerning a clinical intervention, impacts on both proximal and distal outcome variables may be relevant. Assessments will be more defensible if results concerning a proximal outcome align with those concerning a corresponding distal outcome. We present a method to assess the coherence of empirical clinical trial results with biologic and mathematical first principles in situations where the intervention can only plausibly impact the distal outcome indirectly via the proximal outcome. The method comprises a probabilistic sensitivity analysis, where plausible ranges for key parameters are specified, resulting in a constellation of plausible pairs of estimated intervention effects, for the proximal and distal outcomes, respectively. Both outcome misclassification and sampling variability are reflected in the method. We apply our methodology in the context of cluster randomized trials to evaluate the impacts of vaccinating healthcare workers on the health of elderly patients, where the proximal outcome is suspected influenza and the distal outcome is death. However, there is scope to apply the method for other interventions in other disease areas.
Substances chimiques
Influenza Vaccines
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4323-4333Informations de copyright
© 2019 John Wiley & Sons, Ltd.
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