Ancillary study management systems: a review of needs

Abstract

BackgroundThe valuable clinical data, specimens, and assay results collected during a primary clinical trial or observational study can enable researchers to answer additional, pressing questions with relatively small investments in new measurements. However, management of such follow-on, “ancillary” studies is complex. It requires coordinating across institutions, sites, repositories, and approval boards, as well as distributing, integrating, and analyzing diverse data types. General-purpose software systems that simplify the management of ancillary studies have not yet been explored in the research literature.MethodsWe have identified requirements for ancillary study management primarily as part of our ongoing work with a number of large research consortia. These organizations include the Center for HIV/AIDS Vaccine Immunology (CHAVI), the Immune Tolerance Network (ITN), the HIV Vaccine Trials Network (HVTN), the U.S. Military HIV Research Program (MHRP), and the Network for Pancreatic Organ Donors with Diabetes (nPOD). We also consulted with researchers at a range of other disease research organizations regarding their workflows and data management strategies. Lastly, to enhance breadth, we reviewed process documents for ancillary study management from other organizations.ResultsBy exploring characteristics of ancillary studies, we identify differentiating requirements and scenarios for ancillary study management systems (ASMSs). Distinguishing characteristics of ancillary studies may include the collection of additional measurements (particularly new analyses of existing specimens); the initiation of studies by investigators unaffiliated with the original study; cross-protocol data pooling and analysis; pre-existing participant consent; and pre-existing data context and provenance. For an ASMS to address these characteristics, it would need to address both operational requirements (e.g., allocating existing specimens) and data management requirements (e.g., securely distributing and integrating primary and ancillary data).ConclusionsThe scenarios and requirements we describe can help guide the development of systems that make conducting ancillary studies easier, less expensive, and less error-prone. Given the relatively consistent characteristics and challenges of ancillary study management, general-purpose ASMSs are likely to be useful to a wide range of organizations. Using the requirements identified in this paper, we are currently developing an open-source, general-purpose ASMS based on LabKey Server (http://www.labkey.org) in collaboration with CHAVI, the ITN and nPOD.

Eight practices for data management to enable team data science

“…2). BLIS is a customized instance of the open-source LabKey [6][7][8][9][10], an application developed to integrate, analyze, and share biomedical research data, including flow cytometry, proteomics, Luminex, ELISpot, ELISA, Nab, rt-PCR, other plate-based assay data, as well as specimen inventory and clinical subject data (Fig. 2).…”

“…To effectively use data science techniques to resolve these translational questions requires innovation in the organization and management of these data. For over a decade the informatics team of the University of Rochester Clinical and Translational Science Institute's Research Data Integration and Analytics group has been developing comprehensive data management workflows for laboratory assays, specimen inventories, and study-specific data using the open-source LabKey platform [6][7][8][9][10]. Early funding for this effort came from several NIAID grants that recognized the need to develop a system to manage and integrate high-throughput genomics and related data from human subjects.…”

Quality control, analysis and secure sharing of Luminex® immunoassay data using the open source LabKey Server platform

“…These improvements are available in LabKey Server v12.3 (released December 2012) and include: multi-curve titration plots for overlaying and comparing results from several runs (shown in Figure 10), the ability to run arbitrary numbers of transform scripts sequentially during data import, enhanced heuristics for identifying filter options for columns in data grids, and tools for anonymizing data for public release. One ongoing focus of development is support for ancillary studies [77], particularly “freezer studies” that leverage valuable, stored specimens to gain novel insights through new experiments, such as Luminex immunoassays.…”

Treinamento de marcha assistida por robô na reabilitação de AVC: um estudo piloto

“…Cinco reduzir custos e otimizar os benefícios dos dados de pesquisas científicas. Esta prática tem sido recomendada atualmente, a fim de otimizar e aumentar a produção científica 48 .…”

“…No geral, os pacientes gostaram dos treinos e mostraram-se entusiasmados e satisfeitos com os resultados. Destacase a importância da realização de estudos auxiliares (ancillary study)48 para…”