Big data and data repurposing - using existing data to answer new questions in vascular dementia research

Fergus N. Doubal, Myzoon Ali, G. David Batty, Andreas Charidimou, Maria Eriksdotter, Martin Hofmann-Apitius, Yun-Hee Kim, Deborah A. Levine, Gillian Mead, Hermann A. M. Mucke, Craig W. Ritchie, Charlotte J. Roberts (+4 others)
2017 BMC Neurology  
Traditional approaches to clinical research have, as yet, failed to provide effective treatments for vascular dementia (VaD). Novel approaches to collation and synthesis of data may allow for time and cost efficient hypothesis generating and testing. These approaches may have particular utility in helping us understand and treat a complex condition such as VaD. Methods: We present an overview of new uses for existing data to progress VaD research. The overview is the result of consultation with
more » ... various stakeholders, focused literature review and learning from the group's experience of successful approaches to data repurposing. In particular, we benefitted from the expert discussion and input of delegates at the 9 th International Congress on Vascular Dementia (Ljubljana, 16-18 th October 2015). Results: We agreed on key areas that could be of relevance to VaD research: systematic review of existing studies; individual patient level analyses of existing trials and cohorts and linking electronic health record data to other datasets. We illustrated each theme with a case-study of an existing project that has utilised this approach. Conclusions: There are many opportunities for the VaD research community to make better use of existing data. The volume of potentially available data is increasing and the opportunities for using these resources to progress the VaD research agenda are exciting. Of course, these approaches come with inherent limitations and biases, as bigger datasets are not necessarily better datasets and maintaining rigour and critical analysis will be key to optimising data use.
doi:10.1186/s12883-017-0841-2 pmid:28412946 pmcid:PMC5392951 fatcat:redd5kv3qbharbwrds4ikvz32m