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PMCALPLOT: Stata module to produce calibration plot of prediction model performance


  • Joie Ensor

    () (Keele University)

  • Kym IE. Snell

    (Keele University)

  • Emma C. Martin

    (University of Leicester)


pmcalplot produces a calibration plot of observed against expected probabilities for assessment of prediction model performance. pmcalplot can now handle prediction models with binary, survival or continuous outcome types. Calibration is plotted in groups across the risk spectrum as recommended in the TRIPOD guidelines, and confidence intervals for the groupings can also be displayed (NB: not for continuous outcomes). Further, a spike plot of the distribution of events and non-events can be displayed on the plot, as well as a lowess smoother allowing assessment of the calibration at the individual patient level [NB: Spike plot and lowess smoother for survival outcomes is work in progress]. For continuous outcomes a histogram of observed and expected values can be displayed on the corresponding axes. Additionally, common prediction model performance statistics can also be displayed on the plot, quantifying the models performance. pmcalplot is primarily useful for assessment of model performance in an external validation of an existing model. However it can also be used during model development to check the apparent performance of the model (which should show perfect calibration).

Suggested Citation

  • Joie Ensor & Kym IE. Snell & Emma C. Martin, 2018. "PMCALPLOT: Stata module to produce calibration plot of prediction model performance," Statistical Software Components S458486, Boston College Department of Economics, revised 04 Jan 2020.
  • Handle: RePEc:boc:bocode:s458486
    Note: This module should be installed from within Stata by typing "ssc install pmcalplot". The module is made available under terms of the GPL v3 ( Windows users should not attempt to download these files with a web browser.

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