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In designing external validation studies of clinical prediction models,contemporary sample size calculation methods are based on the frequentistinferential paradigm. One of the widely reported metrics of model performanceis net benefit (NB), and the relevance of conventional inference around NB as ameasure of clinical utility is doubtful. Value of Information methodologyquantifies the consequences of uncertainty in terms of its impact on clinicalutility of decisions. We introduce the expected value of sample information(EVSI) for validation as the expected gain in NB from conducting an externalvalidation 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 ofcurrent information and future study's sample size. Value of Informationmethodology provides a decision-theoretic lens to the process of planning avalidation study of a risk prediction model and can complement conventionalmethods when designing such studies.

The expected value of sample information calculations for external validation of risk prediction models