We outline the use of hierarchical modeling for inference about the categorization of subjects into `responder' and `non-responder' classes when the true status of the subject is latent (hidden). If uncertainty of classification is ignored during analysis then statistical inference may be unreliable. An important advantage of hierarchical modeling is that it facilitates the correct modeling of the hidden variable in terms of predictor variables and hypothesized biological relationships. This allows researchers to formalize inference that can address questions about why some subjects respond and others do not. We illustrate our approach using a recent study that looked at hepcidin excretion in female marathon runners(14).