RE: PK models for rich and sparse sampling

From:Nick Holford n.holford@auckland.ac.nz Subject: RE: [NMusers] PK models for rich and sparse sampling Date:Sun, May 30, 2004 10:40 pm Mark, IMHO (FLA>TLA) the undocumented PRIOR in NONMEM V is a poor way to use prior information when you have the full data available. Necessarily the estimates from a prior data set are only a limited description of the true prior data. Plus they will be wrong because all models (and especially NONMEM models) are wrong. Plus the use of the (wrong) SEE as the uncertainty of the prior estimate has no strong justification. Using all the prior data is the most informative Bayesian approach I can think of. Note that the NONMEM PRIOR method is not really Bayesian (Gisleskog et al. 2003). The role for NONMEM PRIOR (or other true Bayesian methods using prior parameters) is when you have some estimates from a prior data set but the data itself is no longer available. I suppose execution time might be a problem if you use all the prior data and you only have slow computers :-) Nick Gisleskog PO, Karlsson MO, Beal SL. Use of Prior Information to Stabilize a Population Data Analysis. Journal of Pharmacokinetics & Biopharmaceutics 2003;29(5/6):473-505 -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x86730 fax:373-7556 http://www.health.auckland.ac.nz/pharmacology/staff/nholford/