Abstract
An Erratum has been published for this article in Health Economics 14(5) 2005, 486.
Cost‐effectiveness analysis (CEA) in health care is increasingly conducted alongside multicentre and multinational randomised controlled clinical trials (RCTs). The increased use of stochastic CEA is designed to account for between‐patient sampling variability in cost‐effectiveness data assuming that observations are independently distributed. However, between‐location variability in cost‐effectiveness may result if there is a hierarchical structure in the data; that is, if there is correlation in costs and outcomes between patients recruited in particular locations. This may be expected in multi‐location trials given that centres and countries often differ in factors such as clinical practice, patient case‐mix and the unit costs of delivering health care. A failure to acknowledge this feature may lead to misleading conclusions in a trial‐based economic study. Multilevel modelling (MLM) is an analytical framework that can be used to handle hierarchical cost‐effectiveness data. Using data from a recently conducted economic analysis, this paper shows how multilevel modelling can be used to obtain (a) more appropriate estimates of the population average incremental cost‐effectiveness and associated standard errors compared to standard stochastic CEA; and (b) location‐specific estimates of incremental cost‐effectiveness which can be used to explore appropriately the variability between centres/countries of the cost‐effectiveness results. Copyright © 2004 John Wiley & Sons, Ltd.