WT as significant covariate on peripheral volume

Dear users, I am finding weight as a covariate on peripheral volume and objective function is dropping more than on central volume in a two compartment model. I am not sure if this is meaningful, I appreciate your comments. Regards, Ayyappa

Hi Ayyappa, did you try testing it on both volumnws at the same time? Would make more sense to me. Regards Sven Am 12.08.2012 um 01:20 schrieb Ayyappa Chaturvedula <[email protected]>: > Dear users, > > I am finding weight as a covariate on peripheral volume and objective > function is dropping more than on central volume in a two compartment model. > I am not sure if this is meaningful, I appreciate your comments. > > Regards, > Ayyappa >

Sven, I tried that and although obj fun reduces by 10 points, theta estimate was very small for central volume and do not make any big difference with increased total body weight. Regards, Ayyappa Sent from my iPad

Dear Ayyappa,What about the physicochemical properties of the drug ? Is the drug lipophilic ?If yes, couldn't this mean that "higher body weight = increased fat = increased volume in which the drug distributes or dissolves" ?Kind Regards,Ahmed Abbas SuleimanDepartment of Pharmacology - Clinical Pharmacology UnitHospital of the University of Cologne (AöR)Gleueler Strasse 24, 50931 Köln, GermanyPhD Candidate at Bonn University, Germany

Hi Ayyappa, As Sven mentioned, it would make the most sense for it to be a significant covariate on both central and peripheral volumes. Just because the objective function value drops more doesn't mean that you shouldn't include the effect of weight on central volume in your final model. The peripheral volume yielding a greater change on OBJ could mean several different things including: • Physiologically, the portion of the body that is not at rapid equilibrium with plasma is more dependent on weight than the portion of the body that is at rapid equilibrium. • Study design(s), you have more data at times after the in the peripheral compartment is dominant (i.e. after the alpha phase of elimination). • Mathematically, the error model has a smaller relative variance at the times with the peripheral compartment is dominant. All three of these are likely inter-mixed to give the true answer. As a general note, just because something isn't statistically significant doesn't mean that you can't include it in your model. Sometimes, you will want to include biologically logical components in your model independent of the statistics. Many people will include weight on all volumes linearly and weight^0.75 on clearance without testing the significance because of the biological relevance. Thanks, Bill