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，当前的样本量计算方法基于频率论推断范式。净收益（net benefit, NB）是广泛报道的模型性能指标之一，但围绕NB作为临床效用度量的传统推断的相关性存疑。信息价值方法通过量化不确定性对临床决策效用的影响来评估其后果。我们提出外部验证的样本信息期望值（expected value of sample information, EVSI）概念，定义为通过开展特定规模的外部验证研究所能获得的NB期望增益。我们提出了EVSI的算法，并通过案例研究展示了EVSI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设计过程提供了决策理论视角，并可在设计此类研究时作为传统方法的补充。