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Classifying individuals as physiological responders using hierarchical modeling

Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand

Submitted 12 December 2007 ; accepted in final form 13 May 2008

We outline the use of hierarchical modeling for inference about the categorization of subjects into "responder" and "nonresponder" 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 of hepcidin excretion in female marathon runners (Roecker L, Meier-Buttermilch R, Brechte L, Nemeth E, Ganz T. Eur J Appl Physiol 95: 569–571, 2005).

hierarchical model; Bayesian inference