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Sequential rank agreement methods for comparison of ranked lists

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The comparison of alternative rankings of a set of items is a general
and prominent task in applied statistics. Predictor variables are
ranked according to magnitude of association with an outcome, prediction
models rank subjects according to the personalized risk of an
event, and genetic studies rank genes according to their difference in
gene expression levels. This article constructs measures of the agreement
of two or more ordered lists. We use the standard deviation
of the ranks to define a measure of agreement that both provides an
intuitive interpretation and can be applied to any number of lists even
if some or all are incomplete or censored. The approach can identify
change-points in the agreement of the lists and the sequential changes
of agreement as a function of the depth of the lists can be compared
graphically to a permutation based reference set. The usefulness of
these tools are illustrated using gene rankings, and using data from
two Danish ovarian cancer studies where we assess the within and
between agreement of different statistical classification methods.
Original languageEnglish Statistics
Pages (from-to)1-23
Number of pages23
StatePublished - 27 Aug 2015


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