In current clinical research, repeated measures in a single subject are common. The problem with repeated measures is that they are closer to one another than unrepeated measures. If this is not taken into account, then data analysis will lose power. In the past decade, user-friendly statistical software programs such as SAS and SPSS have enabled the application of mixed models as an alternative to the classical general linear model for repeated measures with, sometimes, better sensitivity. The objective was to assess whether in studies with repeated measures, designed to test between-subject differences, the mixed model performs better than does the general linear model. In a parallel group study of cholesterol-reducing treatments with 5 evaluations per patient, the mixed model performed much better than did the general linear model with P values of 0.0001 and 0.048, respectively. In a crossover study of 3 treatments for sleeplessness, the mixed model and general linear model performed similarly well with P values of 0.005 and 0.010. Mixed models do, indeed, seem to produce better sensitivity of testing, when there are small within-subject differences and large between-subject differences and when the main objective of your research is to demonstrate between- rather than within-subject differences. The novel mixed model may be more complex. Yet, with modern user-friendly statistical software, its use is straightforward, and its software commands are no more complex than they are with standard methods. We hope that this article will encourage clinical researchers to make use of its benefits more often.