Combining heterogeneous patient-level data into tranSMART to support multicentre studies

Many medical studies have been conducted aiming for better understanding of the causes of diseases and to assist in treatments and protective factors. In some cases, these studies do not produce impactful findings due to the small number of participants. Some initiatives already invested efforts in conducting multicentre studies, which raises other technical challenges due to the heterogeneity of datasets. The analysis of such data sources implies dealing with different data structures, terminologies, concepts, languages, and most importantly, the knowledge behind the data. In this paper, we present a methodology to centralise different datasets into the tranSMART application, using a harmonising strategy based on standard data schema. This methodology can help researchers to generate evidence from a wider variety of data sources. This proposal was validated using Alzheimer's Disease cohorts from several countries, combining at the end 6,669 subjects and 172 clinical concepts. The harmonised datasets can provide multi-cohort queries and analysis. The software package is available, under the MIT license, at https://github.com/bioinformatics-ua/tranSMART-migrator.