Use of Nomograms for Personalized Decision-Analytic Recommendations

Alex Z. Fu, Scott B. Cantor and Michael W. Kattan
Additional contact information
Alex Z. Fu: Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio
Scott B. Cantor: Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas
Michael W. Kattan: Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, kattanm@ccf.org

Medical Decision Making, 2010, vol. 30, issue 2, 267-274

Abstract: Objective. A difficulty with applying decision analysis at the bedside is that it generally requires computer software for the calculations, which may render the method impractical. The purpose of this study was to illustrate the feasibility of developing a regression model that approximates the results from a published decision-analytic model for prostate cancer and permits bedside generation of personalized decision-analytic recommendations with a paper nomogram. Methods. The authors used the example of radical prostatectomy v. watchful waiting for patients with early-stage prostate cancer. First, they took a published decision analysis and generated recommendations using simulated data where patient baseline factors and preference scores for health states were systematically varied. Multivariable logistic regression was used to identify the parameters with strong associations with the recommendation. A reduced model was fit that excluded other preference scores except for watchful waiting. They compared the recommended management predictive accuracies from the full v. reduced model at the individual patient level for 63 men from another published study. Discrimination was assessed using receiver operating characteristic (ROC) curve analysis. A nomogram was constructed from the covariates in the reduced model. Results. The reduced logistic regression model predicted the recommendations accurately for the 63 patients, with an area under the ROC curve of 0.92. Discrimination was excellent as demonstrated by histograms. Conclusions. The authors demonstrated that logistic regression modeling allows accurate reproduction of decision-analytic recommendations with simplified calculations, which can be accomplished using a graphic nomogram. This approach should facilitate clinical decision analysis at the bedside.

Keywords: nomogram; decision analysis; decision making; prostate cancer. (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X09342278 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:30:y:2010:i:2:p:267-274

DOI: 10.1177/0272989X09342278

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().