Department of Mathematics, Indiana University Northwest, Gary, Indiana, USA.
Division of Medical Hematology and Oncology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA.
J Eval Clin Pract. 2023 Dec;29(8):1271-1278. doi: 10.1111/jep.13915. Epub 2023 Aug 25.
Decision curve analysis (DCA) helps integrate prediction models with treatment assessments to guide personalised therapeutic choices among multiple treatment options. However, the current versions of DCA do not explicitly model treatment effects in the analysis but implicitly or holistically assess therapeutic benefits and harms. In addition, the existing DCA cannot allow the comparison of multiple treatments using a standard metric.
To develop a generalised version of DCA (gDCA) by decomposing holistically assessed net benefits and harms into patient preferences versus empirical evidence (as obtained in the trials, meta-analyses of clinical studies, etc.) to allow individualised comparison of single or multiple treatments using a common metric.
We reformulated DCA by (1) decomposing holistic, implicit utilities into specific utilities related to treatment effects and patient's relative values (RV) about disease outcomes versus treatment harms, (2) explicitly modelling each treatment effect at the level of probabilities and/or utilities (outcomes) in a decision tree, and (3) avoiding scaling effects employed in the original DCA to enable comparison of treatment effects against the common metrics. We used data from a published network meta-analysis of randomised trials to inform the use of statin treatment according to Framingham Risk Model.
We illustrate the analysis by modelling the effects of three statins in the primary prevention of cardiovascular disease. We performed simultaneous comparisons against standard metrics (RV) for all treatments. We examined for which RV values, a predictive model for guiding personalised treatment, outperformed the strategies of treating everyone or treating no one. We found that the magnitude of benefits (efficacy) seems more important than the simple ratio of efficacy/harms.
We describe gDCA for evaluating single or multiple treatments to help tailor therapy toward individual risk characteristics. gDCA further helps integrate the principles of evidence-based medicine with decision analysis.
决策曲线分析（DCA）有助于将预测模型与治疗评估相结合，以指导在多种治疗选择中进行个性化治疗决策。然而，当前版本的 DCA 在分析中并未明确建模治疗效果，而是隐含或整体评估治疗的获益和危害。此外，现有的 DCA 无法使用标准指标来比较多种治疗方法。
通过将整体评估的净获益和危害分解为患者偏好与经验证据（如临床试验、荟萃分析等中获得的证据），开发一种通用的 DCA（gDCA）版本，以使用通用指标对单一或多种治疗方法进行个体化比较。
我们通过以下方式重新制定 DCA：（1）将整体的、隐含的效用分解为与治疗效果和患者对疾病结局与治疗危害的相对价值（RV）相关的特定效用；（2）在决策树中明确建模每个治疗效果的概率和/或效用（结局）；（3）避免原始 DCA 中使用的缩放效应，以实现治疗效果与通用指标的比较。我们使用来自已发表的随机试验网络荟萃分析的数据，根据 Framingham 风险模型为他汀类药物治疗提供信息。
我们通过模拟三种他汀类药物在心血管疾病一级预防中的作用来演示分析。我们针对所有治疗方法进行了针对标准指标（RV）的同时比较。我们检查了对于哪些 RV 值，用于指导个体化治疗的预测模型优于治疗所有人或不治疗任何人的策略。我们发现，获益（疗效）的大小似乎比疗效/危害的简单比值更重要。
我们描述了用于评估单一或多种治疗方法的 gDCA，以帮助根据个体风险特征定制治疗方案。gDCA 进一步有助于将循证医学原则与决策分析相结合。
