Title: A Full Bayesian Model to Handle Structural Ones and Missingness in Economic Evaluations from Individual-Level Data,
Abstract: Economic evaluations from individual-level data are an important component of
the process of technology appraisal, with a view to informing resource
allocation decisions. A critical problem in these analyses is that both
effectiveness and cost data typically present some complexity (e.g. non
normality, spikes and missingness) that should be addressed using appropriate
methods. However, in routine analyses, simple standardised approaches are
typically used, possibly leading to biased inferences. We present a general
Bayesian framework that can handle the complexity. We show the benefits of
using our approach with a motivating example, the MenSS trial, for which there
are spikes at one in the effectiveness and missingness in both outcomes. We
contrast a set of increasingly complex models and perform sensitivity analysis
to assess the robustness of the conclusions to a range of plausible missingness
assumptions. This paper highlights the importance of adopting a comprehensive
modelling approach to economic evaluations and the strategic advantages of
building these complex models within a Bayesian framework.