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https://www.selleckchem.com/products/congo-red.html Non-linear mixed effect models are widely used and increasingly integrated into decision-making processes. Propagating uncertainty is an important element of this process, and while standard errors (SE) on pa- rameters are most often computed using asymptotic approaches, alternative methods such as the bootstrap are also available. In this article, we propose a modified residual parametric bootstrap taking into account the different levels of variability involved in these models. The proposed approach uses samples from the individual conditional distribution, and was implemented in R using the saemix algorithm. We performed a simulation study to assess its performance in different scenarios, comparing it to the asymptotic approximation and to standard bootstraps in terms of coverage, also looking at bias in the parameters and their SE. Simulations with an Emax model with different designs and sigmoidicity factors showed a similar coverage rate to the parametric bootstrap, while requiring less hypotheses. Bootstrap improved coverage in several scenarios compared to the asymptotic method especially for the variance param-eters. However, all bootstraps were sensitive to estimation bias in the original datasets. The conditional bootstrap provided better coverage rate than the traditional residual bootstrap, while preserving the structure of the data generating process. The conditional bootstrap provided better coverage rate than the traditional residual bootstrap, while preserving the structure of the data generating process.Mental health and addiction (MHA) related Emergency Department (ED) visits have increased significantly in recent years. Studies identified that a small subgroup of patients constitutes a disproportionally large number of visits. However, there is limited qualitative research exploring the phenomenon from the perspectives of patients who visited ED frequently for MHA reasons, and healthcare provide

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