Use of Nomograms for Personalized Decision-Analytic Recommendations

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.