Cost-effectiveness analysis using data from multinational trials: the use of bivariate hierarchical modelling

Healthcare cost-effectiveness analysis (CEA) often uses individual patient data (IPD) from multinational randomised controlled trials. Although designed to account for between-patient sampling variability in the clinical and economic data, standard analytical approaches to CEA ignore the presence of between-location variability in the study results. This is a restrictive limitation given that countries often differ in factors that could affect the results of CEAs, such as the availability of healthcare resources, their unit costs, clinical practice, and patient case-mix.We advocate the use of Bayesian bivariate hierarchical modelling to analyse multinational cost-effectiveness data. This analytical framework explicitly recognises that patient-level costs and outcomes are nested within countries. Using real life data, we illustrate how the proposed methods can be applied to obtain (a) more appropriate estimates of overall cost-effectiveness and associated measure of sampling uncertainty compared to standard CEA; and (b) country-specific cost-effectiveness estimates which can be used to assess the between-location variability of the study results, while controlling for differences in country-specific and patient-specific characteristics.It is demonstrated that results from standard CEA using IPD from multinational trials display a large degree of variability across the 17 countries included in the analysis, producing potentially misleading results. In contrast, ‘shrinkage estimates’ obtained from the modelling approach proposed here facilitate the appropriate quantification of country-specific cost-effectiveness estimates, while weighting the results based on the level of information available within each country.We suggest that the methods presented here represent a general framework for the analysis of economic data collected from different locations.