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10 March 2017 Re-use of pilot data and interim analysis of pivotal data in MRMC studies: a simulation study
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Abstract
Novel medical imaging devices are often evaluated with multi-reader multi-case (MRMC) studies in which radiologists read images of patient cases for a specified clinical task (e.g., cancer detection). A pilot study is often used to measure the effect size and variance parameters that are necessary for sizing a pivotal study (including sizing readers, non-diseased and diseased cases). Due to the practical difficulty of collecting patient cases or recruiting clinical readers, some investigators attempt to include the pilot data as part of their pivotal study. In other situations, some investigators attempt to perform an interim analysis of their pivotal study data based upon which the sample sizes may be re-estimated. Re-use of the pilot data or interim analyses of the pivotal data may inflate the type I error of the pivotal study. In this work, we use the Roe and Metz model to simulate MRMC data under the null hypothesis (i.e., two devices have equal diagnostic performance) and investigate the type I error rate for several practical designs involving re-use of pilot data or interim analysis of pivotal data. Our preliminary simulation results indicate that, under the simulation conditions we investigated, the inflation of type I error is none or only marginal for some design strategies (e.g., re-use of patient data without re-using readers, and size re-estimation without using the effect-size estimated in the interim analysis). Upon further verifications, these are potentially useful design methods in that they may help make a study less burdensome and have a better chance to succeed without substantial loss of the statistical rigor.
Conference Presentation
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Weijie Chen, Frank Samuelson, Berkman Sahiner, and Nicholas Petrick "Re-use of pilot data and interim analysis of pivotal data in MRMC studies: a simulation study", Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101360H (10 March 2017); doi: 10.1117/12.2255029; https://doi.org/10.1117/12.2255029
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