The main objective of this thesis is advancing the methodology of validation studies of case-finding algorithms that exploit diversity across available data, rather than collecting new data.
The case study that led us to this advancement was the assessment of the capacity of the Italian administrative database to capture cases of chronic disease to get estimates for the compliance with standards of care. Primary care medical records were the main comparative source. Part I of this thesis is focussed on this topic.
In Part II we exploited the results and extended the methodology of Part I to the context of multi-database, multi-national studies.

Additional Metadata
Promotor M.C.J.M. Sturkenboom (Miriam) , N.S. Klazinga (Niek) , M.J. Schuemie (Martijn)
Publisher Erasmus University Rotterdam
Sponsor The research described in this thesis was supported by the Agenzia regionale di sanità della Toscana, through the projects MATRICE and VALORE funded by the Italian Ministry of Health, and through the EMIF project funded by the Innovative Medicines Initiative.
ISBN 978-94-6332-077-1
Persistent URL hdl.handle.net/1765/93461
Citation
Gini, R. (2016, October 14). Validation Odyssey: From big data to local intelligence. Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/93461