In 2002, the American Heart Association recommended a 17-segment model for use in the imaging of the left ventricle. This 17-segment model produces multiple observations per subject, and in turn, these observations can be considered at the segment level, regional level (e.g., slices of the LV or by arterial geometry), or the global level. Appropriate statistical methods depend on number of observations per subject, which depends on the level at which the researcher wishes to analyze data. Many researchers opt to employ data reduction in the 17-segment model using a summary statistic, such as the average of all segments; however, we propose retaining all 17 observations to preserve the correlation between segments. We will first discuss frequentist methods appropriate for global-level, regional-level, and segmental-level data, including two-sample t-tests and generalized linear models. Next, we will discuss a family of covariance structures for clustered data, including a specification developed for use in left ventricular data. We will conclude with an application to left ventricular imaging data from a clinical trial.