Secondary analysis of large databases for hepatology research

Philip N. Okafor, Maria Chiejina, Nicolo De Pretis, Jayant A. Talwalkar

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations


Secondary analysis of large datasets involves the utilization of existing data that has typically been collected for other purposes to advance scientific knowledge. This is an established methodology applied in health services research with the unique advantage of efficiently identifying relationships between predictor and outcome variables but which has been underutilized for hepatology research. Our review of 1431 abstracts published in the 2013 European Association for the Study of Liver (EASL) abstract book showed that less than 0.5% of published abstracts utilized secondary analysis of large database methodologies. This review paper describes existing large datasets that can be exploited for secondary analyses in liver disease research. It also suggests potential questions that could be addressed using these data warehouses and highlights the strengths and limitations of each dataset as described by authors that have previously used them. The overall goal is to bring these datasets to the attention of readers and ultimately encourage the consideration of secondary analysis of large database methodologies for the advancement of hepatology.

Original languageEnglish (US)
Pages (from-to)946-956
Number of pages11
JournalJournal of hepatology
Issue number4
StatePublished - Apr 1 2016


  • Health care delivery research
  • Health services research
  • Liver diseases
  • Outcomes research

ASJC Scopus subject areas

  • Hepatology


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