i'm l o'clock or like to tell you about work the colleagues and i have
done to identify when large medical data bases are suitable places to conduct comparative effectiveness
research
the introduce some graphics
a user capital sigma to designate a provider it's as a nation side because doctors
add up data and come to decisions
a question mark a device a patient
in american cancer patient comes to something that he wants fix
a doctor brings experience in training
the doctor elicits information
the doctor uses all the available information
the country treatment decisions
adaptation can elsewhere a different doctor mitre brought different training and experience to bear because
to a different decision
another doctor might have made yet another decision
and so on
let's imagine for simplicity that all transcribers choose one of two treatments
the same patient stands an equal chance of getting treatment a or treatment be
depending only on which doctor you happens to visit treating community is in the corpus
our analysis screens population medical data for a coke voice
we propose that where we find it we found the suitable place for doing comparative
affection of this research
these graphs illustrate the technique with community acquired pneumonia
the x-axis or patients according to their probability of getting the treatment shown in that
this is called a preference score
the x-axis to pick seventy patients there are at each preference score level
the two lines each crap represent the patient preference scores for the two three bits
exam
the left and graph comparison use only fluxes in is it true motion at different
preference scores
at the midpoint of preference we have patients room that reading communities in perfect a
corpus
how to either side
we can skip channels about the vehicle points
so it should what lots of getting one treatment or the other
among patients receive closer mizuno marks of lexus and fewer fell into this range of
wealth of the corpus
if not patient characteristics predict treatment
then the to treatment groups will be alike
here's a situation that leave a fluxes and is it from us in patients
the two groups are very similar
okay treatment failure
the patients receive leave of losses and had fewer failures
they did better
this is finding deserve ad hoc research we believe that that's
our tools identified one treatment decision for which at least one prescribe or community seems
to be of divided opinion
i difference of twenty percent improvement failure rates would be important of true
the tools shown that so this population is suitable for comparative effectiveness research in that
many patient characteristics are balanced and so we're not confounded
but patients are more the totality of the recorded characteristics
two patients to appear to be similar with respect to the recorded characteristics differ with
respect to their unrecorded characteristics
proper research requires that all important covariance the measured accounted for
keepers of large medical databases can easily apply our tool to identify situations of the
parrot empirical a corpus
we can now when compared of effectiveness research might be successful
and when we should point
thank you for your attention