Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling.

ABSTRACT: Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and there is a lack of diagnostics for when these limitations occur. In this work, a method based on sampling importance resampling (SIR) is proposed, which has the advantage of being free of distributional assumptions and does not require repeated parameter estimation. To perform SIR, a high number of parameter vectors are simulated from a given proposal uncertainty distribution. Their likelihood given the true uncertainty is then approximated by the ratio between the likelihood of the data given each vector and the likelihood of each vector given the proposal

SUBMITTER: Dosne AG 

PROVIDER: S-EPMC5110709 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature