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

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  • Niels Henrik Bruun

    (Aalborg University Hospital)

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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.

Suggested Citation

  • Niels Henrik Bruun, 2025. "CONFREG: Stata module to compute confusion matrix (Accuracy measures) estimated by mixed regression and nlcom," Statistical Software Components S459545, Boston College Department of Economics.
    • Handle: RePEc:boc:bocode:s459545
    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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    File URL: http://fmwww.bc.edu/repec/bocode/c/confreg.ado
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    File URL: http://fmwww.bc.edu/repec/bocode/c/confreg.sthlp
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