CONFREG: Stata module to compute confusion matrix (Accuracy measures) estimated by mixed regression and nlcom

Niels Henrik Bruun ()
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Niels Henrik Bruun: Aalborg University Hospital

Statistical Software Components from Boston College Department of Economics

Abstract: Sensitivity and specificity for a single modality is estimated by regressing the binary values from the modality on the gold standard using mixed regression and robust variance estimation. The AUC is the mean of the sensitivity and the specificity. There are non-linear formulas for estimating the PPV, NPV, and accuracy using prevalence, sensitivity, and specificity. To model more modalities measured on the same patients, we stack the values of each modality and the pathology and add a categorical modality variable. Sensitivity and specificity are estimated using the stacked dataset by regressing the modality values on the pathology values and the categorical modality variable, with robust variance estimation and random intercepts by patient. The AUC, PPV, NPV, and accuracy are estimated from the prevalence, sensitivity, and specificity as described before.

Language: Stata
Requires: Stata version 15.1
Keywords: confusion matrix; accuracy measures (search for similar items in EconPapers)
Date: 2025-11-30
Note: This module should be installed from within Stata by typing "ssc install confreg". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/c/confreg.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/c/confreg.sthlp help file (text/plain)

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