Hi R users friends,

I come now asking for sugestions on a particular problem on a study desing
and analysis for a diagnostic strategy. Last year I started project which
its intent is to validate a combination of signs and symptoms and history of
exposure for a particular tropical disease. After reviewing some strategies
for analysis I decided to use the most simple one such as sensitivity and
specificity and more coplex one such as a combined nomogram and a decision
tree (probably cart) and possibly a neural network. This project started and
still recruting patients.

last week, a collegue of mine, which works wiht urinary tract infection
following patients with spinal cord injure ask my help to develop a similar
project. To validate a combination of signs symptom and a few coplementary
lab tests which can most predict the presence or absence of urinary tract
infection on these patients. However, the analysis approach of a decision
tree and nomogram may not work well due to two problmes. At start a most
crucial problem... what would be a good gold standart if urine culture may
show only colonization but not infection? Well, so far I thought that a
bayesian approach may workout fine. Any other sugestions?

Another crucial problem is that these patients with spinal chord injure and
neurogenic bladder have lots UTI a year. And that can be a lot different
from patient to patient (some have 2 and some have 12 infections a year)
depending of the amount of urine rettained in bladder.

If we include patients everytime they have a suspicious UTI then a patient
with a more severe rettention would be included more often and most likely
his (or her) signs and symptoms would be overestimated for UTI prediction!

If we include only one suspicius episode of each patients then the worst
problem of theses patients, which is repeated UTI over time, might not be
worked out as good as it should.

If I have to choose between one of these, I would choose the first one
trying to incorporate in the analysis the number of past UTI (in the last
six months for example). However Im not shure which would be the best
analysis strategies, perhaps the bayesian approach, even if Im not that
familiar with it, would be the most appropriate! Any suggestions here?

Best regards to all,
-- 
Abraço forte e que a força esteja com você,
Pedro Emmanuel

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