Abstract
We present a general Bayesian framework for cost-effectiveness analysis (CEA) from clinical trial data. This framework allows for very flexible modelling of both cost and efficacy related trial data. A common CEA technique is established for this wide class of models through linking mean efficacy and mean cost to the parameters of any given model. Examples are given in which efficacy may be measured as a continuous, binary, ordinal or time-to-event outcome, and in which costs are modelled as distributed normally, log-normally, as a mixture or non-parametrically. A case study is presented, illustrating the methodology and illuminating the role of prior information.
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