April 1, 2020
TFS forms partnership to conduct a global RWE study on COVID-19 pandemic.
TFS joins Observational Health Data Sciences and Informatics (OHDSI) community in a COVID-19 virtual study-a-thon (March 26-29) to inform healthcare decision-making in response to the current global pandemic. With the increasing availability of real-world COVID-19 data worldwide the main purpose of the event is to generate immediate real-world evidence on prioritized questions shared by national governments, public health agencies, health-related institutions, and community members; and to design COVID-19-specific studies that can be validated and available to run when such data is available. (source: ohdsi.org)
TFS partners with IOMED on a recently approved local RWE study, “COVID-19 REAL”. Within this strategic partnership, TFS will analyze data using the standardized tools provided by the OHDSI collaboration. The COVID-19 REAL focuses on helping address current gaps in the management of COVID-19 pandemic. It is a non-interventional, retrospective, database, cohort study based on anonymized and routinelycollected health care data from several Spanish Hospitals. All generated real-world evidence will be placed in the public domain to support and inform current public health efforts. Patient privacy will be protected in all studies.
Coronavirus disease 2019 (COVID-19) pandemic has an urgent need for answering many questions regarding epidemiology and natural history of the disease. Neus Valveny, TFS Operational Strategy Lead Real World Evidence, addresses the importance of real world evidence to complement clinical trial results. “Similarities and differences with other viral infections as well as effectiveness and safety of current preventive or therapeutic measures are extremely important. Models assessing most relevant prognostic factors in the daily practice will be also crucial to help healthcare providers decide which subjects should be prioritized and given more intensive therapies. The full analysis needs to be adjusted for potential confounding and biases inherent to observational research”.
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