TAGS Instructions

The TAGS software was developed by R Pouillot and G. Gerbier, from Agence Française de Sécurité Sanitaire des Aliments (AFSSA), Maisons-Alfort, France. See Pouillot R, Gerbier G, Gardner IA. "TAGS", a program for the evaluation of test accuracy in the absence of a gold standard. Preventive Veterinary Medicine 2002; 53(1-2):67-81. DOI: 10.1016/S0167-5877(01)00272-0.

The TAGS software can be used to estimate the sensitivity of two or more diagnostic tests in the absence of a gold standard, provided two or more populations with differing prevalences can be cross-classified based on diagnostic test results. The algorithm in the TAGS software follows the frequentist paradigm and utilizes Newton-Raphson and EM algorithms to generate maximum likelihood estimates.

Expanded Description

The TAGS software can accomodate not only data of the "2 independent tests, 2 populations"-type, but also higher order combinations of numbers of tests and numbers of populations. Recognizing that in some instances, true prevalence may be known for some populations, TAGS is capable of utilizing "reference population data" (where one or more populations is of known disease status). Parameter estimation using TAGS becomes possible once the number of degrees of freedom given by the data is greater than the number of parameters to be estimated. A goodness-of-fit test and residual correlations, both of which are provided by TAGS output, provide a means of evaluating model adequacy.

The algorithm includes 2 strong assumptions: (i) diagnostic tests are assumed to be conditionally independent, and (ii) test diagnostic values are considered constant when applied to different populations.

Using TAGS

TAGS can be implemented in three distinct ways. A user may (i) submit data and its structure over the internet using an HTML inferface to a Rweb platform, (ii) run TAGS on Linux/Mac/Windows using R, or (iii) run TAGS on a PC using S-PLUS.

R Version of TAGS through HTML

A version of TAGS has been developed for the R statistical programming environment through an HTML page. Data that is submitted will be loaded using an HTML page implemented in Javascript. A form will then be submitted to an Rweb platform. Within minutes, a new web browser window will appear presenting results of the analysis. Nothing other than a web browser is required. Note that it has not been possible to develop bootstrap confidence interval estimation in HTML version, because the time needed for its calculation was too long and the internet connection mostly failed.

Click here to use TAGS HTML


R Version of TAGS (Linux, Mac or Windows)

Another version of TAGS has been developed in R that can be run directly on a computer. The R software can be obtained from the The R Project for Statistical Computing web site. Once you have installed R, following the instructions provided on the R website, it is a simple matter to use TAGS:

     • Run R

     • Download "TAGS.R" file into your R working directory (folder) on your computer

     • Load TAGS function (from R "File" menu, select "Source R code", then direct it to the "TAGS.R" file in your R working directory).

     • Type TAGS() in the R command window and follow the instructions

Click here to download TAGS.R


S-PLUSVersion of TAGS (Windows)

A third version of TAGS has been developed for the S-PLUS 2000 statistical computing environment. To run this version of TAGS, you will need to have access to S-PLUS, available from Insightful (c) Corporation.

Next, you will need to install Prof. Brian Ripley's 'Hessian library' available from: http://www.stats.ox.ac.uk/pub/SWin/. To install the library, extract the file hessian.zip into the directory "\SP2000\library". There is need to open the library; it is automatically done in the function TAGS. It is now a simple matter to install TAGS:

     • Download "TAGS.ssc" to your computer.

     • Start S-PLUS and source the code, either using the function "source".

     • Type source("path_name/tags.ssc") in the command window in S-PLUS.

     • Type TAGS() in the command window.

Click here to download TAGS.ssc