Methods for analyzing cost effectiveness data from cluster randomized trials

Simple item page
dc.contributor.authorBachmann, Maxen_ZA
dc.contributor.authorFairall, Laraen_ZA
dc.contributor.authorClark, Allanen_ZA
dc.contributor.authorMugford, Mirandaen_ZA
dc.date.accessioned2015-10-12T10:53:39Z
dc.date.available2015-10-12T10:53:39Z
dc.date.issued2007en_ZA
dc.description.abstractBACKGROUND: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.en_ZA
dc.identifier.apacitationBachmann, M., Fairall, L., Clark, A., & Mugford, M. (2007). Methods for analyzing cost effectiveness data from cluster randomized trials. <i>Cost Effectiveness and Resource Allocation</i>, http://hdl.handle.net/11427/14173en_ZA
dc.identifier.chicagocitationBachmann, Max, Lara Fairall, Allan Clark, and Miranda Mugford "Methods for analyzing cost effectiveness data from cluster randomized trials." <i>Cost Effectiveness and Resource Allocation</i> (2007) http://hdl.handle.net/11427/14173en_ZA
dc.identifier.citationBachmann, M. O., Fairall, L., Clark, A., & Mugford, M. (2007). Methods for analyzing cost effectiveness data from cluster randomized trials. Cost effectiveness and resource allocation, 5(1), 12.en_ZA
dc.identifier.ris TY - Journal Article AU - Bachmann, Max AU - Fairall, Lara AU - Clark, Allan AU - Mugford, Miranda AB - 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. DA - 2007 DB - OpenUCT DO - 10.1186/1478-7547-5-12 DP - University of Cape Town J1 - Cost Effectiveness and Resource Allocation LK - https://open.uct.ac.za PB - University of Cape Town PY - 2007 T1 - Methods for analyzing cost effectiveness data from cluster randomized trials TI - Methods for analyzing cost effectiveness data from cluster randomized trials UR - http://hdl.handle.net/11427/14173 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/14173
dc.identifier.urihttp://dx.doi.org/10.1186/1478-7547-5-12
dc.identifier.vancouvercitationBachmann M, Fairall L, Clark A, Mugford M. Methods for analyzing cost effectiveness data from cluster randomized trials. Cost Effectiveness and Resource Allocation. 2007; http://hdl.handle.net/11427/14173.en_ZA
dc.language.isoengen_ZA
dc.publisherBioMed Central Ltden_ZA
dc.publisher.departmentDivision of Pulmonologyen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Licenseen_ZA
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en_ZA
dc.sourceCost Effectiveness and Resource Allocationen_ZA
dc.source.urihttp://resource-allocation.biomedcentral.com/en_ZA
dc.subject.otherHealth Policy and Practiceen_ZA
dc.titleMethods for analyzing cost effectiveness data from cluster randomized trialsen_ZA
dc.typeJournal Articleen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceArticleen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Bachmann_analyzing_cost_effectiveness_data_2007.pdf
Size:
561.45 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Journal Articles