Methods for analyzing cost effectiveness data from cluster randomized trials

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.