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HC Observational Comparative Effectiveness Research (OCER)

The working group on analysis of observational health care data will initially focus on the following topics / concepts:

Reported standard errors for observational studies can be much too narrow to be realistic. Information about the total variability of the health care data generating process, from sources such as bias, confounding and other non-sampling errors, needs to be incorporated. For example, which concepts or methods help health outcomes researchers understand / explain the low historical reproducibility rates of results from observational studies?

Single named diseases, e.g. diabetes, may result from multiple, distinct etiologies. Methods that can detect heterogeneous treatment effects, like Local Control or Recursive Partitioning, can be interpreted as revealing these mixtures.

What sorts of simple explanations and visualization tools can help patients and doctors communicate more meaningfully about uncertainty in the effects of treatments and changes in life-style choices?

Sue McDonald's picture

Healthcare Transition Workshop

If you are planning to participate in the
Data-Driven Decisions in Healthcare Transition Workshop, May 9-10
please log on to the SAMSI web site and register today if
possible.
http://www.samsi.info/workshop/2012-13-dddhc-transition-workshop-may-9-1...
Deadline for registrations is Monday, April 29th!

Meeting Date: 
May 9, 2013 - 8:30am - May 10, 2013 - 5:00pm
Lopiano's picture

Paper Discussed today 4/10

Lopiano's picture

Call for Papers

Lopiano's picture

Material from 2/27 Presentation with References

Bob Obenchain's picture

Update on "Fair Comparisons" concepts...

Lopiano's picture

NMF 1.16.2013 Presentation

Lopiano's picture

Four JAMA Papers

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Papers for Tomorrow

Lopiano's picture

NMF Presentation and Papers