Maybe you want to check out the prevalence package
(http://cran.r-project.org/web/packages/prevalence/index.html), and its
development version on GitHub (https://github.com/brechtdv/prevalence/).
When you have two tests, and neither can be considered to be a 'gold
standard' (ie, SE & SP = 100%), Bayesian latent class approaches can
help you to estimate true prevalence, sensitivity and specificity.
Brecht
Good Day All,
I am working with a diagnostic test and comparing the new test to an old
test. Normally I would be able to calculate sensitivity and specificity
quite easily.
However, the 'gold standard' that I am comparing my new diagnostic
with is
really 'gold-plated' in that sometimes the 'gold standard' fails
completely
and I have no data from the 'gold standard' but I might have data from
the
diagnostic test. Of course sometimes my new diagnostic fails but I have
data from my 'gold standard'
To me this really starts moving towards classification but I cannot
seem to
find the appropriate calculations.
Can someone point me to some web resources to determine the appropriate
method to be able to deal with the NULLs ? Resources within the medical
realm would be better (because the rest of the folks would understand
them
better) but not required.
--
Devleesschauwer Brecht
Doctoral Researcher, MVSc DVM
Department of Virology, Parasitology and Immunology
Faculty of Veterinary Medicine
Ghent University
Salisburylaan 133
9820 Merelbeke
Belgium
Telephone: +32 9 264 7328
Mobile (Belgium): +32 476 365743
Mobile (Nepal): +977 9842 223323
E-mail: brecht.devleesschau...@ugent.be
http://users.ugent.be/~bdvleess/
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