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Assessing the calibration of dichotomous outcome models with the calibration belt

Author

Listed:
  • Giovanni Nattino

    (The Ohio State University)

  • Stanley Lemeshow

    (The Ohio State University)

  • Gary Phillips

    (The Ohio State University)

  • Stefano Finazzi

    (IRCCS Istituto di Ricerche Farmacologiche ‘Mario Negri’)

  • Guido Bertolini

    (IRCCS Istituto di Ricerche Farmacologiche ‘Mario Negri’)

Abstract

The calibration belt is a graphical approach designed to evaluate the goodness of fit of binary outcome models such as logistic regression models. The calibration belt examines the relationship between estimated probabilities and ob- served outcome rates. Significant deviations from the perfect calibration can be spotted on the graph. The graphical approach is paired to a statistical test, syn- thesizing the calibration assessment in a standard hypothesis testing framework. In this article, we present the calibrationbelt command, which implements the calibration belt and its associated test in Stata. Copyright 2017 by StataCorp LP.

Suggested Citation

  • Giovanni Nattino & Stanley Lemeshow & Gary Phillips & Stefano Finazzi & Guido Bertolini, 2017. "Assessing the calibration of dichotomous outcome models with the calibration belt," Stata Journal, StataCorp LP, vol. 17(4), pages 1003-1014, December.
    • Handle: RePEc:tsj:stataj:v:17:y:2017:i:4:p:1003-1014
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    Cited by:

    1. Dimitris Zavras, 2021. "Feeling Uncertainty during the Lockdown That Commenced in March 2020 in Greece," IJERPH, MDPI, vol. 18(10), pages 1-10, May.
    2. Kellner, Adrian & Martinussen, Pål Erling & Feiring, Eli, 2023. "Don't stand so close to me: Perceptions of others’ compliance with COVID-19 recommendations and support for strict policy measures in Norway," Health Policy, Elsevier, vol. 136(C).
    3. Rahmouni, Mohieddine, 2023. "Corruption and corporate innovation in Tunisia during an economic downturn," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 314-326.

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