Revisiting sample size planning for receiver operating characteristic studies: A confidence interval approach with precision and assurance
Di Shu, Guangyong Zou
Abstract
Estimation of areas under receiver operating characteristic curves and their differences is a key task in diagnostic studies. Here we develop closed-form sample size formulas for such studies with a focus on estimation rather than hypothesis testing, by explicitly incorporating pre-specified precision and assurance, with precision denoted by the lower limit of confidence interval and assurance denoted by the probability of achieving that lower limit. For sample size estimation purposes, we introduce a normality-based variance function for valid estimation allowing for unequal variances of observations in the disease and non-disease groups. Simulation results demonstrate that the proposed formulas produce empirical assurance probability close to the pre-specified assurance probability and empirical coverage probability close to the nominal level. Compared with a frequently used existing variance function, the proposed function provides more accurate and efficient sample size estimates. For an illustration of the proposed formulas, we present real-world worked examples. To facilitate implementation, we have developed an online calculator openly available at https://dishu.page/calculator/.