  When using multiple data sources in an analysis, it is important to
understand the influence of each data source on the analysis and the
consistency of the data sources with each other and the model. We suggest the
use of a retrospective value of information framework in order to address such
concerns. Value of information methods can be computationally difficult. We
illustrate the use of computational methods that allow these methods to be
applied even in relatively complicated settings.
  In illustrating the proposed methods, we focus on an application in
estimating the size of hard to reach populations. Specifically, we consider
estimating the number of injection drug users in Ukraine by combining all
available data sources spanning over half a decade and numerous sub-national
areas in the Ukraine. This application is of interest to public health
researchers as this hard to reach population that plays a large role in the
spread of HIV. We apply a Bayesian hierarchical model and evaluate the
contribution of each data source in terms of absolute influence, expected
influence, and level of surprise. Finally we apply value of information methods
to inform suggestions on future data collection.
