Population PK models aim to describe the change in drug concentration over time for a specific population. The populations in population PK modelling often refer to subjects in a clinical trial of a potential drug candidate. Population PK models are frequently described by non-linear mixed effect (NLME) models, that including both random and fixed effect components. The fixed effect components 𝜽 (THETA) portray typical parameter values in the population while the random effects components 𝜼 (ETA) allow for the incorporation of inter-individual variability (IIV) on the typical population value. The IIVs are therefore an important element of NLME models, but the estimation of the IIVs can be time consuming and become a limiting factor for more complex models. Linear approximation of the IIV’s has been suggested as a way to reduce the estimation time whilst maintaining robustness. The aim of this project was to evaluate and compare the estimation time and robustness of the IIVs for the linear approximation of parameter estimation errors in NLME models compared to those estimated in non-linear models. Population PK NLME models were developed for two datasets of phenobarbital and moxonidine. The datasets contained different levels of complexity such as number of subjects, datapoints and route of administration. The models were developed within R-studio using the assembler and Pharmpy packages and evaluated in NONMEM 7.5. Based on the objective function values (OFVs), obtained in the model building processes, selected models were linearised using Pearl speaks NONMEM (PsN). The estimated 𝜀′𝑠 and run-time of the linearised models were compared to their non-linearized counterparts. For all the models a reduction in run-time could be observed but with a slight variation in the estimations between the linearised and non-linearised models. The biggest run time reduction was seen in the oral transit compartment models for moxonidine with a 3100-fold reduction in estimation time. The estimation time reduction displayed could more quickly provide valuable information regarding the chosen error models of more complex models and while parameters estimated may not be identical to the non-linearised models, they should be sufficient during the model building phase.