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6 April 2005 Probabilistic foundations of the MRMC method
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Current approaches to ROC analysis use the MRMC (multiple-reader, multiple-case) paradigm in which several readers read each case and their ratings are used to construct an estimate of the area under the ROC curve or some other ROC-related parameter. Standard practice is to decompose the parameter of interest according to a linear model into terms that depend in various ways on the readers, cases and modalities. It is assumed that the terms are statistically independent (or at least uncorrelated). Bootstrap methods are then used to estimate the variance of the estimate and the contributions from the individual terms in the assumed expansion. Though the methodological aspects of MRMC analysis have been studied in detail, the literature on the probabilistic basis of the individual terms is sparse. In particular, few papers state what probability law applies to each term and what underlying assumptions are needed for the assumed independence. This paper approaches the MRMC problem from a mechanistic perspective. For a single modality, three sources of randomness are included: the images, the reader skill and the reader uncertainty. The probability law on the parameter estimate is written in terms of three nested conditional probabilities, and random variables associated with this probability are referred to as triply stochastic. The triply stochastic probability is used to define the overall average of any ROC parameter as well as certain partial averages of utility in MRMC analysis. When this theory is applied to estimates of an ROC parameter for a single modality, it is shown that the variance of the estimate can be written as a sum of three terms, rather than the four that would be expected in MRMC analysis. The usual terms in MRMC expansions do not appear naturally in multiply-stochastic theory. A rigorous MRMC expansion can be constructed by adding and subtracting partial averages to the parameter of interest in a tautological manner. In this approach the parameter is decomposed into a sum of four random uncorrelated, zero-mean random variables, with each term clearly defined in terms of conditional probabilities. When the variance of the expansion is computed, however, numerous subtractions occur, and there is no apparent advantage to computing the variance term by term; the final result is the same as one gets from the triply stochastic decomposition, at least for the Wilcoxon estimator. No other nontrivial MRMC expansion appears to be possible.
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Harrison H. Barrett, Matthew A. Kupinski, and Eric Clarkson "Probabilistic foundations of the MRMC method", Proc. SPIE 5749, Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment, (6 April 2005);

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