Re: sample size issue

Hi Pete - You can do this through trial simulation/estimation or through information theoretic approaches (e.g. POPT, PFIM, PopED, etc.). Both methods will give you an estimate of expected parameter estimation precision under a given design. Simulation/estimation approaches will also provide an estimate of parameter estimation bias. The challenge is to accurately select the parameters (and model) a priori. If you choose a model with fixed point estimates of THETA, OMEGA and SIGMA, then your conclusions are only valid if the model and parameters are an accurate representation of the truth. Since you are extrapolating to a new population you could run into trouble in this regard; there may considerable uncertainty in the extrapolation of your current parameters to the new population. A more useful approach would be to conduct the simulations or information theory analyses over a joint probability distribution representing uncertainty in the model parameters (and maybe the model itself). For information theoretic methods, PopED allows you to do something like this (I don't mean to leave out other approaches that may also accommodate this). For simulation-estimation methods, you can implement this level of parameter uncertainty at the inter-trial level using simulation tools like Trial Simulator, or the R functions we've developed for simulation from uncertainty distributions in NONMEM (NMSUDS: http://metruminstitute.org/downloads/index.shtml ). The joint uncertainty distributions can be derived from Bayesian posterior distributions, bootstrap results or just an educated guess about plausible distributions encompassing the uncertainty in parameters due to the extrapolation to the new population. For each trial replicate, you'll get an estimate of parameter precision (and bias), resulting in a probability distribution of trial outcomes. You can then examine the sensitivity of the outcome (e.g. %precision of typical CL) to the uncertainty in your simulation parameters (and even model) by plotting trial outcome vs. the trial- specific draws of a given parameter from its uncertainty distribution. Do this for all parameters in your model. If there are regions of these sensitivity curves that do not achieve the desired target response, you could 1) modify the trial design to make it robust enough to achieve the target across the distribution of parameter uncertainty or 2) gain more information about the model parameters (e.g. a pilot study in the new population), reducing the range of uncertainty, and re-run the simulation exercise to determine if the proposed design is sufficient given the improved estimates of model parameters. You'll have to balance practical considerations in either case. In my experience, this sort of request is not new, especially in populations such as pediatrics. Some recent poster presentations on this topic are listed here and are available for download at http:// metrumrg.com/publications.htm: 1. Gastonguay MR, Gibiansky L. Acknowledging and Incorporating Uncertainty in Model-Based Inferences. ECPAG Conference (2006) Workshop Poster Session, Abstract. 2. Gibiansky L and MR Gastonguay. R/NONMEM Toolbox for Simulation from Posterior Parameter (Uncertainty) Distributions. L. Gibiansky and M.R. Gastonguay. PAGE ( 2006) Abstract 958. 3. Mondick JT, Gibiansky L, Gastonguay MR, Veal GJ, Barrett JS. Acknowledging parameter uncertainty in the simulation-based design of an actinomycin-D pharmacokinetic study in pediatric patients with Wilms’ Tumor or rhabdomyosarcoma. PAGE 15 (2006) Abstract 938. 4. Gastonguay MR, Gibiansky L. Acknowledging Parameter Uncertainty by Simulating from Posterior Distributions with NONMEM and R. MUFPADA Annual Meeting (2006) Abstract. 2005 5. Gastonguay MR, El-Tahtawy A. Modeling and Simulation Guided Design of a Pediatric Population Pharmacokinetic Trial for Hydromorphone. The AAPS Journal. Vol. 7, No. S2, Abstract W5318, 2005. Hope this helps. Marc Marc R. Gastonguay, Ph.D. President & CEO, Metrum Research Group LLC [www.metrumrg.com] Scientific Director, Metrum Institute [www.metruminstitute.org] Email: [EMAIL PROTECTED] Direct: +1.860.670.0744 Main: +1.860.735.7043