RE: modeling binary observations

Hello Dr. Zhang, The first reference below discusses some issues and interpretations using CAUC to model binary (0/1) events. In general, CAUC, being non-mechanistic, can have trouble identifying sources of delay, which can have impact on decision-making. Also, CAUC would have difficulty predicting the responses of subjects should they be withdrawn from medication (unless some function were used to un-accumulate the AUC). In the case that we studied, it did provide reasonable predictions of the responses. So one might infer from this that covariate analysis used for certain predictions might not be problematic. The second reference provides a method for constructing semi-mechanistic (?) models. The method is based on the idea of an unobserved (or unobservable) continuous variable. The semi-mechanistic models are built with respect to this 'latent' variable. This method, given sufficient data, should be able to parttion delay between PK (keo) or PD (kin,kout) sources, which could have an impact on decision making and dosing strategies. 1.) Hutmacher, Matthew M., Nestorov, Ivan, Ludden, Tom, Zitnik, Ralph, Banfield, Christopher Modeling the Exposure-Response Relationship of Etanercept in the Treatment of Patients With Chronic Moderate to Severe Plaque Psoriasis J Clin Pharmacol 2007 47: 238-248 2.) Exposure-response modeling using latent variables for the efficacy of a JAK3 inhibitor administered to rheumatoid arthritis patients Matthew M. Hutmacher, Sriram Krishnaswami and Kenneth G. Kowalski Volume 35, Number 2 / April, 2008 Hope these help. Kind regards, Matt