Nomographic Representation of Logistic Regression Models: A Case Study Using Patient Self-Assessment Data

Stephan Dreiseitl, Andrea Harbauer, Michael Binder, Harald Kittler

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Logistic regression models are widely used in medicine, but difficult to apply without the aid of electronic devices. In this paper, we present a novel approach to represent logistic regression models as nomograms that can be evaluated by simple line drawings. As a case study, we show how data obtained from a questionnaire-based patient self-assessment study on the risks of developing melanoma can be used to first identify a subset of significant covariates, build a logistic regression model, and finally transform the model to a graphical format. The advantage of the nomogram is that it can easily be mass-produced, distributed and evaluated, while providing the same information as the logistic regression model it represents.

Original languageEnglish
Pages (from-to)389-394
Number of pages6
JournalJournal of Biomedical Informatics
Volume38
Issue number5
DOIs
Publication statusPublished - Oct 2005

Keywords

  • Logistic regression model
  • Nomogram
  • Nomographic decision aid
  • Patient self-assessment

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