RE: Simulation with uncertainty

Dear Dinko, As Jakob already mentioned, the assumptions we are making when simulating with parameter uncertainty using the variance-covariance matrix or the bootstrap are the same as when we use these techniques to get confidence intervals around model parameters. For the covariance matrix, we assume the parameter vectors arise from a multivariate normal distribution given by the asymptotic covariance matrix. For the bootstrap, parameter vectors arise from each bootstrapped dataset, so there is no assumption about a global parameter distribution. Both of these methods can have drawbacks, in particular when one is far from asymptotic conditions (problematic for covariance matrices) or when datasets have few individuals, many stratas, or when the bootstrap is not problematic (see Niebecker et al. PAGE 2013). Another alternative to simulate with parameter uncertainty is to use Sampling Importance Resampling, which I presented at PAGE this year. The principle is to simulate parameter vectors from the covariance matrix, but add a step where they are evaluated on the original data. Weights are then assigned to each parameter vector representing how likely they are given the data at hand, and based on these weights you can sample parameter vectors to use for simulation with uncertainty. Best regards, Anne-Gaëlle ------------------------------------------------------- Anne-Gaëlle Dosne, PharmD, PhD student Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University PO Box 591 - 751 24 Uppsala - Sweden Mobile: +46 725 859 870 Email: [email protected] --------------------------------------------------------