ViPAR: a software platform for the Virtual Pooling and Analysis of Research Data

Abstract

Background: Research studies exploring the determinants of disease require sufficient statistical power to detect meaningful effects. Sample size is often increased through centralized pooling of disparately located datasets, though ethical, privacy and data ownership issues can often hamper this process. Methods that facilitate the sharing of research data that are sympathetic with these issues and which allow flexible and detailed statistical analyses are therefore in critical need. We have created a software platform for the Virtual Pooling and Analysis of Research data (ViPAR), which employs free and open source methods to provide researchers with a web-based platform to analyse datasets housed in disparate locations. Methods: Database federation permits controlled access to remotely located datasets from a central location. The Secure Shell protocol allows data to be securely exchanged between devices over an insecure network. ViPAR combines these free technologies into a solution that facilitates ‘virtual pooling’ where data can be temporarily pooled into computer memory and made available for analysis without the need for permanent central storage. Results: Within the ViPAR infrastructure, remote sites manage their own harmonized research dataset in a database hosted at their site, while a central server hosts the data federation component and a secure analysis portal. When an analysis is initiated, requested data are retrieved from each remote site and virtually pooled at the central site. The data are then analysed by statistical software and, on completion, results of the analysis are returned to the user and the virtually pooled data are removed from memory. Conclusions: ViPAR is a secure, flexible and powerful analysis platform built on open source technology that is currently in use by large international consortia, and is made publicly available at [ http://bioinformatics.childhealthresearch.org.au/software/vipar/ ].

Optimising the use of linked administrative data for infectious diseases research in Australia

“…the Secure Unified Research Environment established by the Sax Institute in 2012 9 ), or creation of virtual pooled datasets for complex analyses, from individual study locations through a secure web portal (e.g. ViPAR 10 and DataSHIELD 11 ). These data-sharing mechanisms need to be embraced by the research community, data custodians and linkage units.…”

DataSHIELD – New Directions and Dimensions

“…DataSHIELD Wolfson et al 2010), ViPAR (Carter et al 2016). Table 1: Commonly used processes to access biomedical microdata (summarised from Burton et al 2015).…”

“…Brown et al 2010a;Brown et al 2010b) and open source software (e.g. Carter et al 2016;Narasimhan et al 2017). The Canadian Network for Observational Drug Effect Studies (CNODES, Suissa et al 2012) and Mini-Sentinel (a safety surveillance system developed by the U.S. Food and Drugs Administration, Platt and Carnahan, 2012) are both platforms to facilitate the running of analysis requests from approved users locally, along with disclosure checks, prior to securely combining the results centrally as a meta-analysis.…”

“…ViPAR uses an alternative co-analysis approach, utilising a central server to securely virtually pool data from distributed sources into memory to perform an analysis, deleting them on completion (Carter et al 2016). It enables greater analytic flexibility for researchers than DataSHIELD, allowing them to maintain control of their analysis, with the ability to analyse anonymised data using scripts written in open source (R) and licensed (SAS, STATA) statistical software.…”

Privacy, security, and the public health researcher in the era of electronic health record research

“…In such systems, data live in a secured distributed system and the questions of those data (i.e., the analysis) are asked remotely [14][15][16][17]. While this may be attractive from an institutional perspective, the statistical and analytic techniques provided in existing solutions may be insufficient to perform typical epidemiological analyses.…”