Re: population size and confidence power

From: "Nick Holford" n.holford@auckland.ac.nz Subject: Re: [NMusers] population size and confidence power Date: Mon, April 4, 2005 3:48 pm NONMEM (note spelling) is not designed to directly compute power. However, it is possible to use NONMEM (via simulation) to estimate the power of a design to test a particular hypothesis. IMHO any 'a priori' power prediction requires the user to specify: 1. The model parameters (CL, V, Emax, EC50, etc) and the effect size of interest e.g. 30% difference in CL in a sub-population or Emax with some particular value. 2. The random effect size e.g. 50% apparent CV in CL, (and V etc) plus 10% residual error. 3. The hypothesis testing procedure e.g. likelihood ratio test 4. A design e.g. 20 subjects with samples taken at 6 specified times 5. A model e.g. one compartment disposition with bolus input and immediate drug effect described by an Emax model Once you have thought about the problem and you can specify all these features you are in a position to explore the power of the design by varying the number of subjects in the design to see how power varies. You can use NONMEM to simulate a large number of studies with a particular design and then test the hypothesis for each simulated study. If 80 out of 100 such studies fail to reject the null hypothesis then you could conclude that the power of the design is about 80%. Your question is a bit ambiguous and perhaps you have something else in mind e.g. you want to estimate a confidence interval for a parameter of the model. The most robust method for doing this with NONMEM is to use a bootstrap approach (see http://wfn.sourceforge.net/wfnbs.htm for some background on how this might be done). Or perhaps you are interested in deciding which model is most suitable for making predictions of response. The posterior predictive check and similar procedures that use the model to simulate predicted values may be helpful (Yano et al 2001). Nick Yano Y, Beal SL, Sheiner LB. Evaluating pharmacokinetic/pharmacodynamic models using the posterior predictive check. J Pharmacokinet Pharmacodyn 2001;28(2):171-92 -- 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/