Decision curve analysis (DCA) is a popular approach for assessing biomarkers and risk models, but does not require costs and thus cannot identify optimal risk thresholds for actions. Full decision analyses can identify optimal thresholds, but typically used methods are complex and often difficult to understand. We develop a simple framework to calculate the incremental net benefit for a single-time screen as a function of costs (for tests and treatments) and effectiveness (life-years gained). We provide simple expressions for the optimal cost-effective risk threshold and, equally importantly, for the monetary value of life-years gained associated with the risk threshold. We consider the controversy over the risk threshold to screen women for mutations in BRCA1/2. Importantly, most, and sometimes even all, of the thresholds identified by DCA are infeasible based on their associated dollars per life-year gained. Our simple framework facilitates sensitivity analyses to cost and effectiveness parameters. The proposed approach estimates optimal risk thresholds in a simple and transparent manner, provides intuition about which quantities are critical, and may serve as a bridge between DCA and a full decision analysis.

中文翻译：

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一个简单的框架，用于确定单个屏幕的最佳成本效益风险阈值：与决策曲线分析的比较

决策曲线分析 (DCA) 是评估生物标志物和风险模型的一种流行方法，但不需要成本，因此无法确定行动的最佳风险阈值。完整的决策分析可以确定最佳阈值，但通常使用的方法很复杂并且通常难以理解。我们开发了一个简单的框架来计算单次筛查的增量净收益，作为成本（用于测试和治疗）和有效性（获得的生命年）的函数。我们提供了最佳成本效益风险阈值的简单表达式，同样重要的是，与风险阈值相关的生命年的货币价值。我们考虑了对筛查女性BRCA1/2突变的风险阈值的争议. 重要的是，DCA 确定的大多数（有时甚至是全部）阈值基于每生命年获得的相关美元数是不可行的。我们的简单框架有助于对成本和有效性参数进行敏感性分析。所提出的方法以简单和透明的方式估计最佳风险阈值，提供关于哪些数量是关键的直觉，并且可以作为 DCA 和完整决策分析之间的桥梁。