Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Methods for analyzing cost effectiveness data from cluster randomized trials
Cost Effectiveness and Resource Allocation, Volume 5, Article 12, Year 2007
Notification
URL copied to clipboard!
Description
Background: Measurement of individuals' costs and outcomes in randomized trials allows uncertainty about cost effectiveness to be quantified. Uncertainty is expressed as probabilities that an intervention is cost effective, and confidence intervals of incremental cost effectiveness ratios. Randomizing clusters instead of individuals tends to increase uncertainty but such data are often analysed incorrectly in published studies. Methods: We used data from a cluster randomized trial to demonstrate five appropriate analytic methods: 1) joint modeling of costs and effects with two-stage non-parametric bootstrap sampling of clusters then individuals, 2) joint modeling of costs and effects with Bayesian hierarchical models and 3) linear regression of net benefits at different willingness to pay levels using a) least squares regression with Huber-White robust adjustment of errors, b) a least squares hierarchical model and c) a Bayesian hierarchical model. Results: All five methods produced similar results, with greater uncertainty than if cluster randomization was not accounted for. Conclusion: Cost effectiveness analyses alongside cluster randomized trials need to account for study design. Several theoretically coherent methods can be implemented with common statistical software. © 2007 Bachmann et al; licensee BioMed Central Ltd.
Authors & Co-Authors
Bachmann, Max Oscar
United Kingdom, Norwich
University of East Anglia, Norwich Medical School
Fairall, Lara R.
South Africa, Cape Town
University of Cape Town Lung Institute
Clark, Allan
United Kingdom, Norwich
University of East Anglia, Norwich Medical School
Mugford, Miranda
United Kingdom, Norwich
University of East Anglia, Norwich Medical School
Statistics
Citations: 4
Authors: 4
Affiliations: 2
Identifiers
Doi:
10.1186/1478-7547-5-12
e-ISSN:
14787547
Study Design
Randomised Control Trial