  Health economic evaluations based on patient-level data collected alongside
clinical trials~(e.g. health related quality of life and resource use measures)
are an important component of the process which informs resource allocation
decisions. Almost inevitably, the analysis is complicated by the fact that some
individuals drop out from the study, which causes their data to be unobserved
at some time point. Current practice performs the evaluation by handling the
missing data at the level of aggregated variables (e.g. QALYs), which are
obtained by combining the economic data over the duration of the study, and are
often conducted under a missing at random (MAR) assumption. However, this
approach may lead to incorrect inferences since it ignores the longitudinal
nature of the data and may end up discarding a considerable amount of
observations from the analysis. We propose the use of joint longitudinal models
to extend standard cost-effectiveness analysis methods by taking into account
the longitudinal structure and incorporate all available data to improve the
estimation of the targeted quantities under MAR. Our approach is compared to
popular missingness approaches in trial-based analyses, motivated by an
exploratory simulation study, and applied to data from two real case studies.
