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ROCMIC: Stata module to estimate minimally important change (MIC) thresholds for continuous clinical outcome measures using ROC curves

Author

Listed:
  • Robert Froud

    () (Queen Mary, University of London)

Abstract

ROCMIC estimates minimally important change (MIC) thresholds using three different methods. The first is the cut-point corresponding to a 45 degree tangent line intersection; this is mathematically equivalent to the point at which the sensitivity and specificity are closest together. The second is the cut-point corresponding to the smallest sum of 1-sensitivity and 1-specificity; this methodology has been proposed by researchers from the EMGO Institute . The third is the cut-point corresponding to the smallest sum of squares of 1-sensitivity and 1-specificity in accordance with Pythagoras' theorem, as has been recommended by the package authors.

Suggested Citation

  • Robert Froud, 2009. "ROCMIC: Stata module to estimate minimally important change (MIC) thresholds for continuous clinical outcome measures using ROC curves," Statistical Software Components S457052, Boston College Department of Economics, revised 24 Oct 2014.
  • Handle: RePEc:boc:bocode:s457052
    Note: This module should be installed from within Stata by typing "ssc install rocmic". 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/r/rocmic.ado
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    File URL: http://fmwww.bc.edu/repec/bocode/r/rocmic.sthlp
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    More about this item

    Keywords

    minimally important change; MIC; ROC curves;

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