A picture speaks volumes when it comes to relaying a message. This also holds true when one is graphically displaying clinical data. Producing a clear visual representation of laboratory results is the most important method of determining the existence of abnormal values, sampling errors, or other confounding factors within a data set. This paper discusses common mistakes that are made in clinical graphs, and gives guidelines for producing plots that can be clearly interpreted by any data reviewer. The SAS macro, %GRAPHME, which was created to identify possible sources of error and display the most efficient vertical scaling of the data of interest, is also introduced.
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