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.

翻译：决策曲线分析可用于确定基于个体化治疗获益模型是否能够改善临床决策。已有研究描述了利用单个随机对照试验数据估计治疗获益的决策曲线分析方法。本研究的主要目标是将决策曲线分析方法扩展到存在多种治疗方案且其疗效证据来自一系列试验（通过网络荟萃分析合成）的场景。我们描述了利用网络荟萃分析中综合的研究证据估计预测模型净收益所需步骤，展示了如何比较个性化治疗决策策略与"不治疗"或"对所有患者采用特定治疗"等一刀切策略的优劣。通过绘制各策略在合理阈值概率范围内的净收益曲线，可揭示最具临床价值的策略。我们将该方法应用于复发缓解型多发性硬化症的网络荟萃分析预测模型，该模型可用于在那他珠单抗、富马酸二甲酯、醋酸格拉替雷和安慰剂间进行选择。我们利用每种可用治疗的多个阈值组合展示了扩展后的决策曲线分析方法。在所检验的阈值范围内，"根据预测模型治疗患者"策略的疗效优于或接近一刀切治疗策略。然而，即使是微小差异在临床决策中也可能具有重要影响。由于个性化模型优势在所有阈值中并不一致，在推广其应用于决策前可能需要改进模型。这一决策曲线分析的新扩展可应用于基于网络荟萃分析的预测模型，以评估其在辅助治疗决策中的价值。