![]() ![]() SIR led to relative standard errors similar to the covariance matrix and SSE. SIR was about 10 times faster than the bootstrap. SIR was also compared with the covariance matrix, bootstrap and stochastic simulations and estimations (SSE). SIR led to appropriate results after 3 iterations on average. The new procedure was tested on 25 real data examples covering a wide range of pharmacokinetic and pharmacodynamic NLMEM featuring continuous and categorical endpoints, with up to 39 estimated parameters and varying data richness. This issue was alleviated in the present work through the development of an automated, iterative SIR procedure. A non-iterative implementation of SIR proved adequate for a set of simple NLMEM, but the choice of SIR settings remained an issue. In a previous publication, sampling importance resampling (SIR) was proposed as a fast and assumption-light method for the estimation of parameter uncertainty. Different methods to assess parameter uncertainty exist, but scrutiny towards their adequacy is low. In nonlinear mixed-effects models (NLMEM) analysis, this uncertainty is derived from the uncertainty around model parameters. Quantifying the uncertainty around endpoints used for decision-making in drug development is essential. ![]()
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