Monte Carlo decision curve analysis using aggregate data
Monte Carlo decision curve analysis using aggregate data
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DOI：
10.1111/eci.12723
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发表时间：
2017-02-01
影响因子：
5.5
通讯作者：
Djulbegovic, Benjamin
中科院分区：
文献类型：
--
作者：
Hozo, Iztok;Tsalatsanis, Athanasios;Djulbegovic, Benjamin
背景决策曲线分析(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，但其应用需要单个患者的数据。蒙特卡罗(MC)方法可以用来模拟个体患者的概率和结果，为DCA的应用提供了一个有吸引力的选择。材料和方法我们构建了一个MC决策模型来模拟感兴趣的个体结果的概率。将这些概率与决策者在关键管理策略之间无动于衷的阈值概率进行对比：治疗全部、不治疗或使用预测模型指导治疗。针对文献中发表的三种决策模型：(I)他汀类药物用于心血管疾病的一级预防，(Ii)临终病人的临终转介和(Iii)前列腺癌手术，我们将DCA与MC模拟数据的结果与基于实际患者个体数据的DCA结果进行了比较。在一定程度上，患者数据DCA被用来为他汀类药物的使用、转介到临终关怀或前列腺手术提供决策依据，结果表明MC DCA也可以使用。只要在已发表的报告中准确描述结果概率分布和治疗效果的聚合参数，MC DCA就会产生与单个患者数据DCA难以区分的结果。结论我们提供了一个简单、易用的模型，该模型可以促进DCA的更广泛应用，并更好地评估仅依赖文献报道的聚合数据的诊断试验和预测模型。
Background 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.Materials and methods 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.Results 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.Conclusions 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.