The following modules contain software, program code, illustrated examples, and associated publications, all of which are provided for the user's convenience.  We endeavor here to make available to a larger audience some of the methods we've developed in addressing issues of analyzing data derived from imperfect diagnostic tests... These programs have mainly been written for use with WinBUGS, to facilitate their use by data analysts.  The WinBUGS 1.4 software may be downloaded free from the WinBUGS Project, though some S-plus and R code is also presented.

UC Legal Disclaimer
    The Regents of the University of California disclaim all warranties with regard to these software, including all implied warranties of merchantability, noninfringement, and fitness for a particular use.  In no event shall the Regents of the University of California be liable for any direct, special, indirect, or consequential damage or any damage whatsoever resulting from the loss of use, data or profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection with the use or performance of these software.  Use at your own risk.  If you do not agree to this, do not use these software.
     The software modules are organized based on the type of data they would be used to analyze.  For example, the module "Diagnostic Test Se and Sp Estimation: 2 independent tests, 2 populations, no gold standard (T.A.G.S.), " is designed to be used with data that consist of two populations (with different prevalences) that are cross-classified based on the results of two diagnostic tests that are independent conditional on disease status. The parameters about which inferences are made incude sensitivity and specificity of each diagnostic test. The module contains an expanded description of the model, instructions for downloading and using "TAGS" software, a viewable pdf version of the Preventive Veterinary Medicine journal article in which TAGS was first described, and a worked example of how to use TAGS to analyze a sample dataset.
     Unless otherwise stated, the models presented below are Bayesian in nature.

Software Modules:

*A New Version of the Beta Buster software will be posted by Mid March*

Prevalence Estimation:

Disease Freedom: 

Diagnostic Test Se and Sp Estimation:

Logistic Regression, Outcome Based on Imperfect Test results

  Updated 1/09/04