Technical Abstract: The mixed linear model (MLM) is currently among the most advanced and flexible statistical modeling techniques and its use in tackling problems in plant pathology has begun surfacing in the literature. The longitudinal MLM is a multivariate extension that handles repeatedly measured data, such as ratings of disease resistance taken across time, which could be useful to plant pathologists. In this study, using an example dataset from a multi-environment trial of northern leaf blight on maize, longitudinal MLM analysis was performed and its utility was examined. For this dataset, 290 maize lines with diverse levels of resistance exhibited highly correlated disease response profiles and daily resistance responses followed an autoregressive pattern of correlation decay. Because it is common practice to convert repeated measures data to area under the disease progress curve (AUDPC), univariate analysis of AUDPC was compared to longitudinal analysis of repeated measurements. Given the high temporal correlation among maize lines studied here, similar results were obtained using both approaches. Based on side-by-side comparisons of the modeling approaches and computer simulation analysis, using AUDPC was an effective way of circumventing the application of a more complex modeling procedure but longitudinal analysis was an information-rich statistical technique providing broader applicability. To aid in the application of longitudinal MLMs, annotated program syntax for model fitting is provided for the software ASReml.