With advances in computing and information technology, large health care research databases are becoming increasingly accessible to investigators across the world. These rich, population-level data sources can serve many purposes, such as to generate “real-world evidence,” to enhance disease phenotyping, or to identify unmet clinical needs, among others. This is of particular relevance to the study of patients with end-stage liver disease (ESLD), a socioeconomically and clinically heterogeneous population that is frequently under-represented in clinical trials. This review describes the recommended “best practices” in the execution, reporting, and interpretation of large database clinical epidemiology research in hepatology. The advantages and limitations of selected data sources are reviewed, as well as important concepts on data linkages. The appropriate classification of exposures and outcomes is addressed, and the strategies needed to overcome limitations of the data and minimize bias are explained as they pertain to patients with ESLD and/or liver transplantation (LT) recipients. Lastly, selected statistical concepts are reviewed, from model building to analytic decision making and hypothesis testing. The purpose of this review is to provide the practical insights and knowledge needed to ensure successful and impactful research using large clinical databases in the modern era and advance the study of ESLD and LT.
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