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A general-purpose nomogram generator for predictive logistic regression models

Author

Listed:
  • Alexander Zlotnik

    (Technical University of Madrid)

  • Victor Abraira

    (Ramon y Cajal University Hospital)

Abstract

Multivariate logistic regression is a statistical method commonly used in several fields to build predictive models. A nomogram is a tool that provides graphical depictions of all variables in the model and enables the user to easily compute output probabilities. Our objective was to build a flexible and easy-to-use nomogram generator in Stata. The script works after arbitrary logit or logistic commands. Copyright 2015 by StataCorp LP.

Suggested Citation

  • Alexander Zlotnik & Victor Abraira, 2015. "A general-purpose nomogram generator for predictive logistic regression models," Stata Journal, StataCorp LP, vol. 15(2), pages 537-546, June.
    • Handle: RePEc:tsj:stataj:v:15:y:2015:i:2:p:537-546
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj15-2/st0391/
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    Citations

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    Cited by:

    1. Amirhossein Jalali & Alberto Alvarez-Iglesias & Davood Roshan & John Newell, 2019. "Visualising statistical models using dynamic nomograms," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-15, November.
    2. Mariana Hatmanu & Christiana Brigitte Sandu & Elisabeta Jaba, 2019. "A Comparative Study on Drivers for Corporate Environmental Responsibility, EU15 vs. EU-NMS13," Sustainability, MDPI, vol. 11(22), pages 1-27, November.
    3. Daniel Homocianu, 2024. "Life Satisfaction: Insights from the World Values Survey," Societies, MDPI, vol. 14(7), pages 1-41, July.
    4. Daniel Homocianu & Octavian Dospinescu & Napoleon-Alexandru Sireteanu, 2022. "Exploring the Influences of Job Satisfaction for Europeans Aged 50 + from Ex-communist vs. Non-communist Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 159(1), pages 235-279, January.
    5. Daniel Homocianu & Dinu Airinei, 2022. "PCDM and PCDM4MP: New Pairwise Correlation-Based Data Mining Tools for Parallel Processing of Large Tabular Datasets," Mathematics, MDPI, vol. 10(15), pages 1-27, July.
    6. Aurelian-Petruș Plopeanu & Daniel Homocianu & Nelu Florea & Ovidiu-Aurel Ghiuță & Dinu Airinei, 2019. "Comparative Patterns of Migration Intentions: Evidence from Eastern European Students in Economics from Romania and Republic of Moldova," Sustainability, MDPI, vol. 11(18), pages 1-21, September.
    7. Daniel Homocianu, 2023. "Exploring the Predictors of Co-Nationals’ Preference over Immigrants in Accessing Jobs—Evidence from World Values Survey," Mathematics, MDPI, vol. 11(3), pages 1-29, February.
    8. Daniel Homocianu & Aurelian-Petruș Plopeanu & Rodica Ianole-Calin, 2021. "A Robust Approach for Identifying the Major Components of the Bribery Tolerance Index," Mathematics, MDPI, vol. 9(13), pages 1-20, July.
    9. Nasrin Borumandnia & Hassan Doosti & Amirhossein Jalali & Soheila Khodakarim & Jamshid Yazdani Charati & Mohamad Amin Pourhoseingholi & Atefeh Talebi & Shahram Agah, 2021. "Nomogram to Predict the Overall Survival of Colorectal Cancer Patients: A Multicenter National Study," IJERPH, MDPI, vol. 18(15), pages 1-11, July.

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