  Patient-level health economic data collected alongside clinical trials are an
important component of the process of technology appraisal, with a view to
informing resource allocation decisions. For end of life treatments, such as
cancer treatments, modelling of cost-effectiveness/utility data may involve
some form of partitioned survival analysis, where measures of health-related
quality of life and survival time for both pre- and post-progression periods
are combined to generate some aggregate measure of clinical benefits (e.g.
quality-adjusted survival). In addition, resource use data are often collected
from health records on different services from which different cost components
are obtained (e.g. treatment, hospital or adverse events costs). A critical
problem in these analyses is that both effectiveness and cost data present some
complexities, including non-normality, spikes, and missingness, that should be
addressed using appropriate methods. Bayesian modelling provides a powerful
tool which has become more and more popular in the recent health economics and
statistical literature to jointly handle these issues in a relatively easy way.
This paper presents a general Bayesian framework that takes into account the
complex relationships of trial-based partitioned survival cost-utility data,
potentially providing a more adequate evidence for policymakers to inform the
decision-making process. Our approach is motivated by, and applied to, a
working example based on data from a trial assessing the cost-effectiveness of
a new treatment for patients with advanced non-small-cell lung cancer.
