{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T14:12:16Z","timestamp":1761487936121},"reference-count":21,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2007,10,22]],"date-time":"2007-10-22T00:00:00Z","timestamp":1193011200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2008,5,30]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Many critical questions in medicine require the analysis of complex multivariate data, often from large data sets describing numerous variables for numerous subjects. In this paper, we describe CoPlot, a tool for visualizing multivariate data in medicine. CoPlot is an adaptation of multidimensional scaling (MDS) that addresses several key limitations of MDS, namely that MDS maps do not allow for visualization of both observations and variables simultaneously and that the axes on an MDS map have no inherent meaning. By addressing these issues, CoPlot facilitates rich interpretation of multivariate data. We present an example using CoPlot on a recently published data set from a systematic review describing clinical features and disease progression of children with anthrax and provide recommendations for the use of CoPlot for evaluating and interpreting other healthcare data sets. 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