Clinical decisions are often guided by clinical prediction models or diagnostic tests. Decision curve analysis (DCA) combines classical assessment of predictive performance with the consequences of using these strategies for clinical decision-making. In DCA, the best decision strategy is the one that maximizes the so-called net benefit: the net number of true positives (or negatives) provided by a given strategy. In this decision-analytic approach, often only point estimates are published. If uncertainty is reported, a risk-neutral interpretation is recommended: it motivates further research without changing the conclusions based on currently-available data. However, when it comes to new decision strategies, replacing the current Standard of Care must be carefully considered -- prematurely implementing a suboptimal strategy poses potentially irrecoverable costs. In this risk-averse setting, quantifying uncertainty may also inform whether the available data provides enough evidence to change current clinical practice. Here, we employ Bayesian approaches to DCA addressing four fundamental concerns when evaluating clinical decision strategies: (i) which strategies are clinically useful, (ii) what is the best available decision strategy, (iii) pairwise comparisons between strategies, and (iv) the expected net benefit loss associated with the current level of uncertainty. While often consistent with frequentist point estimates, fully Bayesian DCA allows for an intuitive probabilistic interpretation framework and the incorporation of prior evidence. We evaluate the methods using simulation and provide a comprehensive case study. Software implementation is available in the bayesDCA R package. Ultimately, the Bayesian DCA workflow may help clinicians and health policymakers adopt better-informed decisions.

翻译：临床决策常依赖于临床预测模型或诊断测试。决策曲线分析（DCA）将预测性能的经典评估与这些策略在临床决策中的应用后果相结合。在DCA中，最佳决策策略是使所谓净收益最大化的策略：即某项策略提供的真阳性（或真阴性）净数量。在这种决策分析方法中，通常仅发布点估计值。若报告不确定性，建议采用风险中性解释：这有助于在不改变基于现有数据结论的前提下推动进一步研究。然而，对于新型决策策略，替代当前标准治疗必须审慎考量——过早实施次优策略可能带来不可挽回的成本。在这种风险规避情境下，量化不确定性还可评估现有数据是否足以支撑改变当前临床实践的证据。我们在此采用贝叶斯方法进行DCA，针对评估临床决策策略时的四个核心问题：（i）哪些策略具有临床实用性，（ii）现有可用的最佳决策策略是什么，（iii）策略间的成对比较，以及（iv）当前不确定性水平下的预期净收益损失。虽然贝叶斯DCA常与频率学派点估计保持一致性，但完全贝叶斯方法允许构建直观的概率解释框架并纳入先验证据。我们通过模拟研究评估方法性能，并提供综合性案例研究。相关软件实现已收录于bayesDCA R包。最终，贝叶斯DCA工作流程或可帮助临床医生和卫生政策制定者做出更明智的决策。