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Causal inference for effectiveness research in using secondary data
Investigator (PI): Schneeweiss, Sebastian
Performing Organization (PO): (Current): Brigham and Women's Hospital, Department of Medicine, Division of Pharmacoepidemiology and Pharmacoeconomics / (617) 278-0930
Supporting Agency (SA): Patient-Centered Outcomes Research Institute (PCORI)
Initial Year: 2013
Final Year: 2018
Record Source/Award ID: PCORI/ME-1303-5638
Funding: Total Award Amount: $1,102,523
Award Type: Contract
Award Information: PCORI: More information and project results (when completed)
Abstract: Patient-centered outcomes research (PCOR) can only be successful with valid analytics. The routine operation of the US health care system produces an abundance of electronically stored data that captures the care of patients as it is provided in settings outside of controlled research environments. The potential for utilizing these data to inform future treatment choices and improve patient care and outcomes of all patients in the very system that generates the data is widely acknowledged. Particularly for elderly multi-morbid patients and most other vulnerable patient groups who are often excluded from randomized trials, these data, properly analyzed, are key to improving care. Further, such secondary data reflect the health outcomes as they occur in routine care, a main goal of effectiveness research. Given these key properties of secondary data and the abundance of electronic health care databases covering millions of patients, it is critical to strengthen the rigor of analyses of such data. Highly innovative analytic approaches have recently been developed that (1) are solidly grounded in the principles of science and (2) are made to best fit any electronic health care data source. With the involvement of top researchers, patients, doctors, and other decision makers we plan to evaluate how much better these new methods perform. To prove this we use several large databases of electronic medical records and health insurance records. We will test the relationship between two newer and frequently used cardiovascular therapies. We will also use computer-generated artificial data in which we can impose a known association. In such simulation studies we can further understand and improve the performance of these new analytic methods. The project will yield guidance on the optimal use and advantages of these new approaches for patient-centered outcome research. It will further improve the performance of these methods in settings important to patients and their doctors.
MeSH Terms:
  • Aged
  • * Comparative Effectiveness Research
  • Computer Simulation
  • Data Collection
  • Decision Making
  • Electronic Health Records
  • Humans
  • * Medical Informatics
  • * Patient-Centered Care
  • Randomized Controlled Trials as Topic
  • Software
  • United States
  • Vulnerable Populations
Country: United States
State: Massachusetts
Zip Code: 02120
UI: 20143575
Project Status: Completed
Record History: ('2018: Project extended to 2018. ',) ('2017: Project extended to 2017.',)