In designing external validation studies of clinical prediction models, contemporary sample size calculation methods are based on the frequentist inferential paradigm. One of the widely reported metrics of model performance is net benefit (NB), and the relevance of conventional inference around NB as a measure of clinical utility is doubtful. Value of Information methodology quantifies the consequences of uncertainty in terms of its impact on clinical utility of decisions. We introduce the expected value of sample information (EVSI) for validation as the expected gain in NB from conducting an external validation study of a given size. We propose algorithms for EVSI computation, and in a case study demonstrate how EVSI changes as a function of the amount of current information and future study's sample size. Value of Information methodology provides a decision-theoretic lens to the process of planning a validation study of a risk prediction model and can complement conventional methods when designing such studies.

翻译：在设计临床预测模型的外部验证研究时，当代样本量计算方法基于频率主义推断范式。模型性能的常用报告指标之一是净收益，而围绕净收益作为临床效用衡量指标的传统推断方法存在局限性。信息价值方法通过量化不确定性对决策临床效用的影响来评估其后果。我们引入验证的样本信息期望值，将其定义为开展特定规模外部验证研究可获得的净收益预期增益。我们提出了样本信息期望值的计算算法，并通过案例研究展示了样本信息期望值如何随当前信息量与未来研究样本量的变化而变化。信息价值方法为风险预测模型验证研究的规划过程提供了决策理论视角，可在设计此类研究时作为传统方法的补充。