The proposed research will investigate and generate methods for the
evaluation of multiple correlated diagnostic test results. The
biostatistical methods themselves are applicable to diverse fields.
This application will focus on analysis of receiver operating
characteristic (ROC) curves generated from diagnostic imaging studies.
ROC analysis is used to compare the capacities of imaging systems (i.e.,
combination of imaging modality and reader) to discriminate between
actually positive and actually negative cases. Diagnostic imaging
studies commonly use designs in which each case is imaged with multiple
modalities and each image is interpreted by several radiologists
independently, to increase the power of comparisons of discrimination
capacities across modalities and to learn about variation in
discrimination capacity across readers. The discrimination capacity of
an imaging system can also depend on characteristics of cases. The
overall goal of this research is to create innovative statistical
methods for evaluating medical diagnostic tests that will be practical
for use in the real world. First, multivariate ordinal regression
models will be developed that allow discrimination capacity to depend
on characteristics of cases while allowing inferences to generalize to
populations of readers (random effects). Second, multivariate ordinal
regression methods that can accommodate the types of missing data that
arise in repeated measurements studies will be developed. Finally,
issues of study design and performance of multivariate ordinal
regression models in studies with small to moderate sample sizes will
be investigated. The ordinal regression models used in this research
will employ a multiplicative predicator such that both the height and
summetry of the ROC curve can depend on characteristics of cases and/or
readers. The proposed research requires difficult innovations in
biostatistical methodology for the analysis of multivariate ordinal
categorical data. The development of new methods and generation of new
ideas and insights will have a significant impact in both the medical
and the biostatistical communities on research in methods for evaluating
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