Don Hoover, Rutgers University
Title: Issues in epidemiological analysis of complex data
Issues In Epidemiological Analyses Of Complex Data - For over 40 years,
multivariate linear, logistic and survival regression models have
identified causal epidemiological associations with notable successes.
Yet conflicting findings from multivariate epidemiological models as well
as those not confirmed in clinical trials have occurred. It is well
known that multivariate model building has potential problems from
unmeasured variables, collinearity, multiple comparison and other issues.
Still, with today's computational resources, as data sets become larger
and the association patterns studied more complex, the potential for such
problems increases. This talk illustrates three recent examples where the
data suggested: 1) the true association pattern was to complex to be
modeled with available data, 2) a major unanticipated effect was
identified in an ancillary analysis and 3) multivariate adjustment
distorted rather than resolved causal association. While analytical
methods may or may not exist to deal with settings such as these, as
analyses become more complex, chances increase that such issues will fail
to be identified.
David Madigan, Rutgers University
Title: Analysis of hospital discharge data
The availability of large-scale hospital inpatient data has
prompted the development of websites and reports that
include "league tables" of hospitals and healthcare providers.
These league tables consider issues such as hospital
mortality, readmission rates, and length-of-stay.
The usefulness of these rankings depends critically
on appropriate "risk adjustment" that accounts for
pre-existing patient risk factors. This talk will
look at one such website for the State of Pennsylvannia
http://www.phc4.org and examine their analytic methods.
Title: Lattice representation of data sets
The talk will focus on the expressivity of an exploratory data analysis
method based on lattice theory.
The method is called Formal Concept Analysis. First the basic notions
surrounding the method will be presented.
Then we will discuss some additional enrichments and insights we have
found upon applying this method,
along with points requiring further development.
Alex Pogel, New Mexico State University
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