Decision curve analysis for personalized treatment choice between multiple options (2202.02102v2)

Abstract: 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 RCT. Our main objective is to extend the decision curve analysis methodology to the scenario where several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA). 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, like "treat none" or "treat all patients with a specific treatment" strategies. The net benefit per strategy can then be plotted for a plausible range of threshold probabilities 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. We illustrated the extended decision curve analysis methodology using several threshold values 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, an improved model may be needed before advocating its applicability for decision-making. This novel extension of decision curve analysis can be applied to NMA based prediction models to evaluate their use to aid treatment decision-making.