Methods for analyzing cost effectiveness data from cluster randomized trials
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BioMed Central
Open Access
Methodology
Methods for analyzing cost effectiveness data from cluster randomized trials Max O Bachmann*1, Lara Fairall2, Allan Clark1 and Miranda Mugford1 Address: 1School of Medicine, Health Policy and Practice, University of East Anglia, Norwich, UK and 2Lara Fairall, Research Fellow, University of Cape Town Lung Institute, University of Cape Town, Cape Town, South Africa Email: Max O Bachmann* - [email protected]; Lara Fairall - [email protected]; Allan Clark - [email protected]; Miranda Mugford - [email protected] * Corresponding author
Published: 6 September 2007 Cost Effectiveness and Resource Allocation 2007, 5:12
doi:10.1186/1478-7547-5-12
Received: 14 May 2007 Accepted: 6 September 2007
This article is available from: http://www.resource-allocation.com/content/5/1/12 © 2007 Bachmann et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract 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.
Background Cluster randomized trials are commonly used to evaluate the effectiveness and cost effectiveness of interventions in health care, health promotion and health professional education. Groups of individuals, such as doctors' patients or schools' pupils, are allocated together to receive different interventions or to follow usual practice. One key advantage of randomly allocating groups rather than individuals is that it permits inferences about the intervention's effects on service providers as well as on
users. For example, in a trial of an educational intervention aimed at doctors,
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