Hozo Iztok, Tsalatsanis Athanasios, Djulbegovic Benjamin
Department of Mathematics, Indiana University, Gary, IN, USA.
USF Health Program for Comparative Effectiveness Research, Division for Evidence-based Medicine, Department of Internal Medicine, University of South Florida, Tampa, FL, USA.
Eur J Clin Invest. 2017 Feb;47(2):176-183. doi: 10.1111/eci.12723.
Decision curve analysis (DCA) is an increasingly used method for evaluating diagnostic tests and predictive models, but its application requires individual patient data. The Monte Carlo (MC) method can be used to simulate probabilities and outcomes of individual patients and offers an attractive option for application of DCA.
We constructed a MC decision model to simulate individual probabilities of outcomes of interest. These probabilities were contrasted against the threshold probability at which a decision-maker is indifferent between key management strategies: treat all, treat none or use predictive model to guide treatment. We compared the results of DCA with MC simulated data against the results of DCA based on actual individual patient data for three decision models published in the literature: (i) statins for primary prevention of cardiovascular disease, (ii) hospice referral for terminally ill patients and (iii) prostate cancer surgery.
The results of MC DCA and patient data DCA were identical. To the extent that patient data DCA were used to inform decisions about statin use, referral to hospice or prostate surgery, the results indicate that MC DCA could have also been used. As long as the aggregate parameters on distribution of the probability of outcomes and treatment effects are accurately described in the published reports, the MC DCA will generate indistinguishable results from individual patient data DCA.
We provide a simple, easy-to-use model, which can facilitate wider use of DCA and better evaluation of diagnostic tests and predictive models that rely only on aggregate data reported in the literature.
决策曲线分析（DCA）是一种越来越多地用于评估诊断试验和预测模型的方法，但其应用需要个体患者数据。蒙特卡罗（MC）方法可用于模拟个体患者的概率和结果，并为DCA的应用提供了一个有吸引力的选择。
我们构建了一个MC决策模型来模拟感兴趣结果的个体概率。将这些概率与决策者在关键管理策略（即全部治疗、不治疗或使用预测模型指导治疗）之间无差异的阈值概率进行对比。我们将基于MC模拟数据的DCA结果与基于文献中发表的三个决策模型的实际个体患者数据的DCA结果进行了比较：（i）他汀类药物用于心血管疾病的一级预防，（ii）为晚期患者转诊至临终关怀机构，以及（iii）前列腺癌手术。
MC DCA和患者数据DCA的结果相同。就使用患者数据DCA为他汀类药物使用、转诊至临终关怀机构或前列腺手术的决策提供信息而言，结果表明也可以使用MC DCA。只要已发表报告中准确描述了结果概率和治疗效果分布的总体参数，MC DCA将产生与个体患者数据DCA无法区分的结果。
我们提供了一个简单、易于使用的模型，它可以促进DCA的更广泛应用，并更好地评估仅依赖文献中报告的总体数据的诊断试验和预测模型。
