Decision Curve Analysis for Personalized Treatment Choice between Multiple Options

Background Decision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial. Objectives Our main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA). Methods We describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as treat none or treat all patients with a specific treatment strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo. Results We illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the treat patients according to the prediction model strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness. Conclusions This novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making.

Automated summary

Decision curve analysis is a method to evaluate personalized treatment models for better clinical decisions. This study extended the analysis to situations with multiple treatment options using network meta-analysis. The results showed that the personalized treatment model performed either better or close to one-size-fit-all treatment strategies, but further improvement is needed to ensure consistent performance across all thresholds.