  Cost-Effectiveness Analyses (CEAs) alongside randomised controlled trials
(RCTs) are increasingly often designed to collect resource use and
preference-based health status data for the purpose of healthcare technology
assessment. However, because of the way these measures are collected, they are
prone to missing data, which can ultimately affect the decision of whether an
intervention is good value for money. We examine how missing cost and effect
outcome data are handled in RCT-based CEAs, complementing a previous review
(covering 2003-2009, 88 articles) with a new systematic review (2009-2015, 81
articles) focussing on two different perspectives. First, we review the
description of the missing data, the statistical methods used to deal with
them, and the quality of the judgement underpinning the choice of these
methods. Second, we provide guidelines on how the information about missingness
and related methods should be presented to improve the reporting and handling
of missing data. Our review shows that missing data in within-RCT CEAs are
still often inadequately handled and the overall level of information provided
to support the chosen methods is rarely satisfactory.
