Classifying individuals as physiological responders using hierarchical modeling.
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).
Department of Mathematics and Statistics, University of Otago, PO Box 56, Dunedin, New Zealand. rbarker@maths.otago.ac.nz
Richard J
RJ
Barker
Matthew R
MR
Schofield
D000328:Adult; D000465:Algorithms; D023181:Antimicrobial Cationic Peptides / Q000097:blood; D003627:Data Interpretation, Statistical; D005260:Female; D064451:Hepcidins; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D010827:Physiology / Q000145:classification*; D012420:Running / Q000502:physiology; D012984:Software
D023181:Antimicrobial Cationic Peptides; C578702:HAMP protein, human; D064451:Hepcidins