Heterogeneity in treatment response is often encountered in clinical research outside the context of pre-defined subgroups. Analytic methods are now available to identify individuals who are more or less responsive to treatment in clinical trials and observational studies. Factor mixture model (FMM) methodology, is designed for data that exhibits heterogeneity in the overall sample but may include several latent subclasses that are themselves homogeneous. FMM combines latent class analysis and common factor analysis. These individuals, once identified, can then be assessed to see if they have common characteristics that may shed light on their differential response to treatment. Identifying heterogeneity in treatment response may play a key role in the development of truly “personalized medicine.”